To learn about OCR

ROS

SLAM

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Circuit Board Plotter, Circuit Board Plotter Suppliers and Manufacturers at Alibaba.com

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Simultaneous Localization and Mapping: Part I History of the SLAM problem | the morning paper

Simultaneous Localization and Mapping: Part I History of the SLAM problem - Durrant-Whyte & Bailey 2006 You can't read far in robotics without encountering the SLAM problem. But what is it exactly? And what are the main approaches to solving it? Today's paper provides an overview of the SLAM problem, a short history of research…

3_mapping.pdf

Index of /~mmcgill/comp3431

SBAS Made Easy | SxblueGPS

ETD collection: Browse by Author - A

Robust vehicle localization in urban environments using probabilistic maps - IEEE Xplore Document

Autonomous vehicle navigation in dynamic urban environments requires localization accuracy exceeding that available from GPS-based inertial guidance system

What are some current research areas in Simultaneous Localization and Mapping (SLAM)? - Quora

The problem of moving sensors

kuemmerle09auro.pdf

What are the state of the art methods in monocular SLAM (Simultaneous Localization And Mapping)? - Quora

SDCM news

sbas_tutorial_V_2_0.pdf

Artificial Intelligence for Robotics Course | Udacity

AI for Robotics, taught by the leader of Google's self-driving car, focuses on programming a robotic car using the Python programming language.

output GPS data onto ROS topics - Stack Overflow

Distributed Computer Systems - lect4.pdf

slam - What is the best way to fuse measurements from IMU, LIDAR, and Encoder information in some recursive bayesian filter? - Robotics Stack Exchange

IMU + LIDAR SLAM approach

Has anyone seen this video?

http://www.gkmm.tu-darmstadt.de/rescue/?q=node/141

Using ROS with an IMU as their odometry and a LIDAR. The results look amazing! I was unable to find any more information. They say on their site they will be posting ROS drivers soon so I thought someone may have heard about this already.

I have an IMU I would like to stick in the XV-11 to see how well this approach works.

-Hash

slam_gmapping using imu data instead of /odom - ROS Answers: Open Source Q&A Forum

Hi all, i am wondering whether we can use gmapping with imu data instead of /odom if yes what would be the pointer in this direction. Thanks (p.s i am using ubuntu 10.04 with ros electric)

NTU SLAM

http://www.exquisitus.eee.ntu.edu.sg/NewsnEvents/Pages/Events-Detail.aspx?news=d1b0bcd8-1b9c-4f79-96e4-f6e24b4a96b2

Wang Han's Home Page

RCLab

Robotic Navigation and Mapping with Radar - Martin Adams, Ebi Jose - Google ブックス

Focusing on autonomous robotic applications, this cutting-edge resource offer you a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. It examines grid based robotic mapping with radar based on measurement likelihood estimation. You find detailed coverage of simultaneous localization and Map Building (SLAM) an area referred to as the Holy Grail of autonomous robotic research. The book derives an extended Kalman Filter SLAM algorithm which exploits the penetrating ability of radar. This algorithm allows for the observation of visually occluded objects, as well as the usual directly observed objects, which contributes to a robots position and the map state update. Moreover, you discover how the Random Finite Set (RFS) provides a more appropriate approach for representing radar based maps than conventional frameworks.

Gamini Dissanayake | University of Technology Sydney

ROS Navigation Basics — ROS Tutorials 0.5.1 documentation

ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues (PDF Download Available)

Official Full-Text Publication: ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues on ResearchGate, the professional network for scientists.

navigation - ROS Wiki

What are all the packages in ROS that I should learn to start R&D in the visual navigation of mobile robots? - Quora

Kobuki紹介記事　第二回　KobukiをROSで使う　その１ | RT Robot Shop Blog

Using robot_localization with amcl [closed] - ROS Answers: Open Source Q&A Forum

Hi all, I am starting to set up awesome robot_localization package and I am using rotary encoders (nav_msgs/Odometry, both pose and twist) and IMU (sensor_msgs/Imu) which provides continuous data and Ultrawide band sensor which provides global absolute position data as a nav_msgs/Odometry message (only pose, (x,y and yaw)) . As suggested on the wiki page of the package, I can fuse data from the encoders and the IMU in the odom_frame which gives the transform odom_frame -> base_link_frame. For the map_frame, I can fuse data from rotary_encoders, IMU and Ultrawide band sensor (It is similar to GPS in the sense that it provides global absolute position data) and it gives the transfrom from map_frame -> odom_frame but next I am using amcl to improve the pose_estimate and amcl also provides map_frame->odom_frame transformation. So, I want to ask how should I deal with the transformations (specifically map_frame->odom_frame), Should I just not use the map_frame->odom_frame transformation from robot_localization and let amcl take care of it, If yes how can I not use the transformation? Or is there any better way to deal with it? Update 1: If I give output of AMCL and UWB to second instance of ekf_localization_node, it will give me the required transformation (map_frame -> odom_frame) and will publish the output odometry message on odometry/filtered topic but I am using move_base which expects robot pose on /amcl_pose topic. So, I can remap the output on odometry/filtered to amcl_pose and change the message type from nav_msgs/Odometry to geometry_msgs/PoseWithCovarianceStamped, Does that sound okay? Also, another approach which I am thinking is to run the first instance of ekf_localization_node (in odom_frame) with rotary encoders and IMU as input which will give odom_frame->base_link_frame transformation and then run second instance of ekf_localization_node (in map_frame) with UWB, rotary encoders and IMU as input. I am slightly confused about how the second instance will work. Will it modify the odom_frame -> base_link_frame tf by considering (or changing) the data from UWB in the odom_frame? If yes, then I can directly use the modified odom_frame->base_link_frame tf (which includes UWB, encoders and IMU) (and ignore map_frame->odom_frame tf from second instance of ekf_localization_node) in amcl and everything should be fine, right? Update 2: Thanks @Tom Moore for the answer! So just to clarify, run the first instance of ekf_localization_node in odom_frame with wheel odometry and IMU data as input which will give us the odom_frame -> base_link_frame transformation. Then run the second instance of the ekf_localization_node in the map_frame with wheel odometry, IMU data, UWB data (it is in map_frame) and amcl_pose which will give us the map_frame -> odom_frame transformation using existing odom_frame -> base_link_frame tf. Thanks in advance. Naman Kumar

ROS-Users - amcl for localization

amcl for localization. Hello, I run everything(roscore,RosAria,laser,gmapping) + I run rosrun map_server map_server map.yaml (map is the map which I created using Robot) this gives following...

ROS_Mapping_Localization_Wiki_namank_dshroff_fi.pdf

cmumrsdproject - Adaptive Monte Carlo Localization (Details of Theory and ROS Package)

navguide.pdf

RPLidar For ROS Based SLAM and Navigation_slamtec_KintZhao.pdf

Stanford University Thesis - Petrovskaya_2011_Thesis.pdf

Localization issue using gmapping and odom data. - ROS Answers: Open Source Q&A Forum

Hi all, I do have a problem with my localization! I use gmapping to build a map and to determine the robots position. Apparently my map (base_frame: /map) and my costmap (base_frame: /odom) do not correlate. Unforeseeable jumps appear. As soon as new space is explored, the map is not really extended, but the robot is shifted to the opposite direction. After a while, the robots position is redetermined and the costmap is shifted back to its "real" position. This seems to be weird and mess up my map. Additionally my robots position is also not correct which is a problem for further processing! as you see in the screenshot ... mapping works fine and also the costmap was right. Then the robot rotates and as soon as new space (beneath the robot) is explored, the costmap makes a jump to the top!! Instead of expanding the underlying map, the costmap is shifted and the robots position is wrongly determined!! The real Position is close to the "/map" tf (same offset as robot to "/odom" tf). Here is my tf tree: Accuracy of my odom data seem to bee right ... Here is a link to a video showing the problem. Hopefully this helps to understand the problem which appears. It can be seen that no /odom /map problems appear as long as the robot moves to the front or backwards, but immediately when it starts to rotate, the mapping gets screwed up and "/map" and "/odom" differ. https://www.dropbox.com/s/v4y6i7s5l0jhy7r/mapping.mpeg Here is my gmapping.launch file: <launch> <node pkg="gmapping" type="slam_gmapping" name="slam_gmapping" output="screen"> <param name="base_frame" value="base_footprint"/> <param name="map_frame" value="map"/> <param name="odom_frame" value="odom"/> <param name="map_update_interval" value="0.1"/> <param name="maxUrange" value="4.0"/> <param name="maxRange" value="5.0"/> <param name="sigma" value="0.05"/> <param name="kernelSize" value="1"/> <param name="lstep" value="0.05"/> <param name="astep" value="0.05"/> <param name="iterations" value="5"/> <param name="lsigma" value="0.075"/> <param name="ogain" value="3.0"/> <param name="lskip" value="0"/> <param name="srr" value="0.01"/> <param name="srt" value="0.02"/> <param name="str" value="0.01"/> <param name="stt" value="0.02"/> <param name="linearUpdate" value="0.01"/> <param name="angularUpdate" value="0.01"/> <param name="temporalUpdate" value="0.1"/> <param name="resampleThreshold" value="0.5"/> <param name="particles" value="1"/> <param name="xmin" value="-5.0"/> <param name="ymin" value="-5.0"/> <param name="xmax" value="5.0"/> <param name="ymax" value="5.0"/> <param name="delta" value="0.05"/> <param name="llsamplerange" value="0.01"/> <param name="llsamplestep" value="0.01"/> <param name="lasamplerange" value="0.005"/> <param name="lasamplestep" value="0.005"/> </node> </launch> FYI, I do not face processing problems due to high update rates. I just want the map to be updated continuously. CPU load is at 60% when running all nodes. Has anyone experienced some similar problems? BR Daniel

http://answers.ros.org/question/9480/is-there-a-good-way-to-verify-my-odometry-data-is-sensible/808%3C/p%3E

So i've got my laserscan and my odometry and i load up amcl and it doesn't seem to be working - it's pose estimates are literally miles off the map. I can load up my laserscans in rviz - they look pretty good. Is there a good way to verify that my odometry drivers are producing sensible output? I can watch them with rostopic echo, but I don't know how to compare the values well. I am hoping that there is an easy/good way to select where i am on the map and then have it just trust my odometry so i can see if they're at all accurate or not.

Questions - ROS Answers: Open Source Q&A Forum

Open source question and answer forum written in Python and Django

HOW to fuse IMU data in Hector SLAM - ROS Answers: Open Source Q&A Forum

Hello Im using Hector SLAM and it works fine. My sensor package consist of IMU, laser and Kinect camera. Would like to know how to use IMU for better pose estimation. I know that pose estimation is based in laser scan matching but Im not using the sources if IMU. So i think fusing this IMU data would gives me better orientation. ANY help on this???Thanks

http://www.geology.smu.edu/~dpa-www/robots/doc/odometry.txt

localization - Hector SLAM, Matching algorithm - Robotics Stack Exchange

W3_T1_Using the Hector SLAM.pdf

hector_slam/Tutorials/SettingUpForYourRobot - ROS Wiki

Yet another problem with robot_localization and amcl - ROS Answers: Open Source Q&A Forum

Hi there, I've read several threads about robot_localization with amcl and odometry data, but I have not being able to make it work. What I did: amcl config file: tf_broadcast: false ekf config file publish_tf: true map_frame: "/map" odom_frame: "/base_odom_link" base_link_frame: "/base_footprint_link" world_frame: "/map" odom0: "/dev/odom" pose0: "/amcl_pose" The odometry/filtered topic is almost the same as the /dev/odom, which means it is filtering the odom_frame. This looked to me as if the ekf could not read the config file and went to its default settings. But I checked the rqt_graph and it listens to the right odom and pose topic. If a set the tf_broadcast of the amcl to true. The scan matches in RVIZ jump between the amcl and ekf tf, which is somehow the same as odom. What am I missing? Edit 1: in response to comments from @Nicholash Bedi odom0_config: [true, true, false, false, false, true, true, true, false, false, false, true, false, false, false] odom0_differential: true odom0_relative: false pose0_config: [true, true, false, false, false, true, false, false, false, false, false, false, false, false, false] pose0_differential: false pose0_relative: false Edit 2: uploaded pics of my config files. Hopefully you can see the error. ekf_config.jpg amcl_config.jpg Edit 3 In response to @Tom Moore -> output of rostopic echo /amcl_pose and /dev/odom. I am sorry for the very very late response odom header: seq: 161063 stamp: secs: 1495189822 nsecs: 753257722 frame_id: base_odom_link child_frame_id: base_footprint_link pose: pose: position: x: -0.0955580770969 y: -0.127791434526 z: 0.0 orientation: x: 0.0 y: -0.0 z: -0.00566518455002 w: -0.999983952713 covariance: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] twist: twist: linear: x: 0.0 y: 0.0 z: 0.0 angular: x: 0.0 y: 0.0 z: 0.0 covariance: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] amcl header: seq: 185 stamp: secs: 1495187483 nsecs: 905225829 frame_id: /map pose: pose: position: x: 2.232329958 y: 4.91519891085 z: 0.0 orientation: x: 0.0 y: 0.0 z: 0.692934073576 w: 0.721000949845 covariance: [0.0028978789516864722, -0.0026280780170626628, 0.0, 0.0, 0.0, 0.0, -0.0026280780170626628, 0.018904477044003443, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0 ... (more)

Yujin Robot

GitHub is where people build software. More than 21 million people use GitHub to discover, fork, and contribute to over 59 million projects.

(42) ros-sig-navigation - Google Groups

Google Groups allows you to create and participate in online forums and email-based groups with a rich experience for community conversations.

Find the differences between robot_localization package and robot_pose_ekf · Issue #44 · correll/advancedrobotics

advancedrobotics - Advanced Robotics class repository

State Estimation and Localization in Robot Navigation - ROS Answers: Open Source Q&A Forum

Hello, I would like to ask the difference between a state estimator like robot_localization, or robot_pose_ekf and a localization package like amcl. Can we say, a state estimator estimating (x, y, z, roll, pitch, yaw, and their respective velocities) is more general than a localization module which only estimates (x, y, z, roll, pitch, yaw) Other than the fact that, robot_localization, or robot_pose_ekf are fusing odometry data from different sensors and amcl is using this data plus laser/camera data to localize the robot, and (x, y, z) in state estimators are relative to the initial position and global (relative to the map) in amcl Thanks

Developing a Rapid Prototyping Framework for Applications that Interface with Sensor Devices | Charles River Analytics

Presented at the Global Health and Performance Summit, Columbus, OH (May 2016)

A Generalized Extended Kalman Filter Implementation for the Robot Operating System | Charles River Analytics

Presented at the 13th International Conference on Intelligent Autonomous Systems (IAS 13), Padova, Italy (July 2014)

Charles River Analytics Releases Software for Open-source Robotics Framework | Charles River Analytics

Software provides 3D robot state estimation through sensor fusion for Robot Operating System

dynamically change the source of transformation(tf) - ROS Answers: Open Source Q&A Forum

Hi all, I have a mobile robot with different sensors on it like Rotary Encoders, IMU, Kinect, Hokuyo Lidar and Ultrawide band. I am using robot_pose_ekf to get the odom_combined -> base_footprint transformation and I have a fixed transformation from base_footprint -> base_link. I have two sources of map->odom_combined transformation : amcl and robot_localization (running with world_frame as map) with inputs from Rotary encoders(odometry data), IMU (They are in the odom_combined frame) and UWB sensor (Its in the map_frame). Now, I have a condition and when it is satisfied, I want to use amcl and when another condition is satisfied, I want to use robot_localization. For example when the robot is in a corridor, I want to use amcl because Laser scan is available whereas if the robot is in a big hall where there is no laser scan because there are no walls within the range of the Hokuyo Lidar (Its range is 5m), I want to use robot_localization (which uses Ultrawide band sensor). So, I want to dynamically change which map->odom_combined transformation source I am using based on my criterion. What is the best way to do something like this? Update 1: a) If I give amcl output as input to robot_localization and in the case when I am not broadcasting amcl (meaning the values on the /amcl_pose topic are not changing), will robot_localization ignore amcl and continue without it? The same thing goes with UWB sensor. b) Also, I am getting absolute pose measurements from amcl and UWB sensor, can I set both of their differential parameters as false? Most of the time, I will be using only one of the inputs (either UWB or amcl) but there is definitely a possibility that there will be times, I will be using both. The problem with setting differential for one of them as true is that when I am using only that particular input, I have no absolute measurement as input to robot_localization. Also, in that case I can not set initial pose of the robot. Update 2: Are you saying that amcl stops sending messages when its output is not changing? No, I meant under the condition when I don't have to use amcl, I can tell amcl not to publish anything on the amcl_pose topic and yes, I will always have wheel odometry and IMU data. To be more clear, I have 4 inputs to the second instance of robot_localization (first instance takes wheel odometry and IMU data and gives odom->base_link tf) : wheel odometry, IMU, UWB sensor and AMCL. The reason I said that I want both UWB and AMCL as absolute (not differential) because in cases where I am using only one of them and it is differential (For example if AMCL differential = true and I am using wheel odometry, IMU and AMCL, then differential for all 3 sources is true), then I don't have any absolute source of pose measurement (I am not sure whether this is okay or not) to ... (more)

particletutorial.dvi - particletutorial.pdf

Yiannis's CIM Home Page

Patente US20140129027 - Simultaneous Localization And Mapping For A Mobile Robot - Google Patentes

A method of simultaneous localization and mapping includes initializing a robot pose and a particle model of a particle filter. The particle model includes particles, each having an associated map, robot pose, and weight. The method includes receiving sparse sensor data from a sensor system of the robot, synchronizing the received sensor data with a change in robot pose, accumulating the synchronized sensor data over time, and determining a robot localization quality. When the accumulated sensor data exceeds a threshold accumulation and the robot localization quality is greater than a threshold localization quality, the method includes updating particles with accumulated synchronized sensor data. The method includes determining a weight for each updated particle of the particle model and setting a robot pose belief to the robot pose of the particle having the highest weight when a mean weight of the particles is greater than a threshold particle weight.

Using AMCL without wheel odometry (only laser + IMU) - ROS Answers: Open Source Q&A Forum

Hello I gave a question regarding using some data from my sensor package. I have a sensor package of IMU, Laser rage finder and Camera in my robot (its actually electric wheelchair). For my robot localisation Im using only individuals laser scans and amcl. But my amcl jumps and my results are not so good. For example when Im calculating some parameters like velocity or distance to some object the results are not so good. Im using IMU data only in for the ICP in the laser scan matcher package. But for other purposes not. So my problem is how to improve the results?? For example for my linear velocity parameters im using exclusive the amcl pose. But how to improve that using IMU data or for example extended kalman filter. I dont have any odometry. My odometry is all based on the laser. In that way is my pose estimated, only using laser and amcl. So how to improve the robot heading, motion estimation based on the sensor package I have. So with laser scans, IMU and camera (for the kinect), how to get a better results in sense of motion estimation, heading, and obtain some parameters like linear and angular velocity. Any help?? Thanks

amcl does not need odometry data? - ROS Answers: Open Source Q&A Forum

I'm reading amcl document on ROS Wiki. In its subscribed topics there is not odometry topic, why? It works only with laser? Subscribed Topics: scan (sensor_msgs/LaserScan) tf (tf/tfMessage) initialpose (geometry_msgs/PoseWithCovarianceStamped) map (nav_msgs/OccupancyGrid)

RO (range-only) Localization with ROS - Google Groups

Google Groups allows you to create and participate in online forums and email-based groups with a rich experience for community conversations.

How mapping using HOKUYO LIDAR (URG-04LX) and hector_slam? - ROS Answers: Open Source Q&A Forum

Hello I've been working on ROS Fuerte for a month in order create a map using only LIDAR (URG/04LX) no odometer with hector slam, without much success, until the moment all I can do is watch the hokuyo_node data displayed in rviz, but when I throw hokuyo_node + hector_slam (tutorial.launch) not any map is created, not to do I'm desperate, someone has to launch file I need is? additional information I'm using a virtual machine Hokuyo connected by usb

UsersGuide-0.9.pdf

Show Us Your Sensors: SLAM

Pepper with google Cartographer - Google Groups

Google Groups allows you to create and participate in online forums and email-based groups with a rich experience for community conversations.

Dieter Fox (University of Washington Seattle, Seattle) on ResearchGate - Expertise: Artificial Intelligence

Dieter Fox of University of Washington Seattle, Seattle with expertise in Artificial Intelligence is on ResearchGate. Read 279 publications, and contact Dieter Fox on ResearchGate, the professional network for scientists.

Karto SLAM Solution from SRI Adds Features and Open-Source Option in Version 2.0 - Robotics Business Review

KARTO mapping now open source and on code.ros.org - ROS robotics news

liulinbo/slam: learning SLAM,curse,paper and others

slam - learning SLAM,curse,paper and others

A flexible and scalable SLAM system with full 3D motion estimation - IEEE Xplore Document

For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online l

dillenberger/pepperl_fuchs: Pepperl+Fuchs R2000 Driver

pepperl_fuchs - Pepperl+Fuchs R2000 Driver

RTAB-Map

RTAB-Map : Real-Time Appearance-Based Mapping

3D slam for autonomous vehicle - Irawiki

3DTK - The 3D Toolkit / Code / [r1394] /trunk/src/slam6d

Reading group - Irawiki

ipa320/cob_environment_perception

Contribute to cob_environment_perception development by creating an account on GitHub.

Research cob

cob_environment_perception/slam_node.cpp at indigo_dev · ipa320/cob_environment_perception

Contribute to cob_environment_perception development by creating an account on GitHub.

Sebastian Thrun's Homepage

FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges

FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges on ResearchGate, the professional network for scientists.

SLAM Algorithm to combine IMU measurements with 2D image or Point Cloud? - ROS Answers: Open Source Q&A Forum

Hi all. We are a research group at Aalborg University currently investigating the use of SLAM for indoor positioning of drones. We have decided to use ROS to test out different implementations and develop our own algorithm+controller. The short version of our question is: Which SLAM algorithm do you recommend for localization using a 2D camera or point cloud data from a depth sensor (RealSense) while also including other sensor measurements (IMU + GPS) The detailed explanation and reason for this question can be seen below. In our search for previous work with SLAM, where the primary goal is real-time localization (pose estimation), we have not been able to find that much. For real-time localization a lot of work has been put into EKF-SLAM and FastSLAM, but mainly focused on 2D/planar navigation using LiDAR sensors Otherwise it seems like a lot of research, especially when using other sensors such as camera and RGBD sensors, is put into the mapping portion of SLAM. In our case we want to focus on the pose estimation and want to have as realiable and robust a position as possible. We would also like to include other sensor information such as attitude estimates (roll, pitch, yaw) from the drone flight controller into the SLAM problem to enhance the positioning. Furthermore we also have an indoor positioning system capable of delivering a position measurement with a slow rate (~1 Hz) but with quite noisy measurement and with a sensor that is likely to dropout in certain areas of the building. Hence we would like to incorporate these measurements as well but not only rely on these why we are investigating SLAM in the first place. Can any of you suggest any previous SLAM work or suggest any usefull paths for us to investigate? At the moment our plan is defined as follows: Extend FastSLAM to support features in 3D space and estimate 6D pose Use either a 2D camera or the point cloud output of a 3D depth sensor (RealSense, similar to Kinect). Investigate how other sensor information can be incorporated into FastSLAM But is this a reasonable plan at all? Best regards, Thomas Jespersen

Drone Publications

Drone Publications

How to calculate covariance matrix? - ROS Answers: Open Source Q&A Forum

I'm trying to use robot_pose_ekf and I have errors: Covariance speficied for measurement on topic wheelodom is zero and filter time older than odom message buffer I've wrote the odometry node and it works fine. It is true that I don't have a covariance matrix for it. I've looked at other ROS answers threads and read the wiki page, but I don't quite understand it or how to calculate the values for it. For example, I've attempted to populate an IMU message: self.imu_msg.header.stamp = rospy.Time.now() self.imu_msg.header.frame_id = '/base' self.imu_msg.orientation_covariance = [-1, 0, 0, 0, -1, 0, 0, 0, -1 ] #sensor doesn't have orientation self.imu_msg.angular_velocity.x = data[x_rps] #sensor outputs in rad/s self.imu_msg.angular_velocity.y = data[y_rps] self.imu_msg.angular_velocity.z = data[z_rps] self.imu_msg.angular_velocity_covariance = [ 0, -1, 0, 0, 0, 0, 0, 0, 0 ] self.imu_msg.linear_acceleration.x = 9.8 * data[x_g] #my sensor output in g's self.imu_msg.linear_acceleration.y = 9.8 * data[y_g] self.imu_msg.linear_acceleration.z = 9.8 self.imu_msg.linear_acceleration_covariance = [ 0, -1, 0, 0, 0, 0, 0, 0, 0 ] self.imu_pub.publish(self.imu_msg) The documentation said fill in a -1 for the first element if I don't know how to populate it. I've tried visualizing it in rviz using the beginner tutorial plug-in, but it just flashes large slabs of purple for me. Using the http://wiki.ros.org/rviz_imu_plugin also had a problem compiling(something about the property_manager.h file not existing). Is that that imu message correct? How would I go calculate it for odom? I'll also have a covariance matrix for the navsat message. Edit 1: I'm working through adding a covariance matrix for odom, imu, and gps. I'm currently using a segway rmp unit, so I was able to pull this from the segway_rmp's covariance values from that source code. http://wiki.ros.org/segway_rmp this->odom_msg.pose.covariance[0] = 0.00001; this->odom_msg.pose.covariance[7] = 0.00001; this->odom_msg.pose.covariance[14] = 1000000000000.0; this->odom_msg.pose.covariance[21] = 1000000000000.0; this->odom_msg.pose.covariance[28] = 1000000000000.0; this->odom_msg.pose.covariance[35] = 0.001; This got rid of the errors for the ekf node. Although, I have no idea why the twist covariance matrix didn't need to be filled. I would still want to know how to find it for my own set up. According to this explanation of covariances, http://manialabs.wordpress.com/2012/08/06/covariance-matrices-with-a-practical-example/ It looks like all we care about in the matrix is the error of a single variable, so x-axis to x-axis, roll-to-roll etc... So in that segway_rmp code, we only care about x, y, and yaw, which makes sense. I did that for my imu node but I still get the errors MSG to TF: Quaternion Not Properly Normalized filter time older than imu message buffer I'll update this as I go! Edit ... (more)

hector localization package against known map - ROS Answers: Open Source Q&A Forum

Hi, I would like to ask, if there is solution from hector group to localize the robot against the known map. As the hector_slam provides excellent localization, but it also builds new map at the same time, on the other hand, the amcl package of ros does not work well with my robot (a segway omni platform), especially compared to the hector_slam, so I try to directly use the hector_slam with the known map (by just ignoring the produced map and only use the localization results), but the map being built does not always match the known map, therefore the localization is not as expected. Thus I need a better solution for this (not only from hector). I use ros-hydro on ubuntu 13.04 32bit. Thanks a lot for viewing! (As far as I know, hector_localization package does not do this job.)

Robot localization with AMCL and EKF - ROS Answers: Open Source Q&A Forum

Hello, I have been following an example here with jackal. My goal so far was to understand how AMCL works. However, in his example he uses ekf_localization and AMCL is just a tool to output cloud with the current robot's position. Here is a rqt_graph when I ran the localization example (which is running gazebo, amcl and rviz) . I thought that AMCL was a separate algorithm for localization and it did not need anything else. What am I missing? Why they are using both EKF and AMCL? Screenshot of rqt_graph: http://tinyurl.com/z73k3nj

Appearance-Based Navigation, Localization, Mapping, and Map Merging for Heterogeneous Teams of Robots - gorkemerincphdthesis.pdf

Critical Rays Scan Match SLAM | SpringerLink

Welcome to Motilal Agrawal's home page

OpenKarto SRI staff

My home page

karto/Tutorials/How to change Karto internal solver (tutorial2.cpp) - ROS Wiki

2011_SSRR_KohlbrecherMeyerStrykKlingauf_Flexible_SLAM_System.pdf

Karto SDK – Karto Robotics

Exteroceptive Sensors - Measuring Proximity

Exteroceptive sensors measure the proximity of objects in relation to the robot's frame of reference.

3DTK - The 3D Toolkit / Code / [r1406] /trunk

MRPT GraphSLAMNikos Koukis | Control Systems Lab

MRPT Graph SLAM Laboratories

mrpt/main_body.tex at master · MRPT/mrpt

mrpt - ⚡ The Mobile Robot Programming Toolkit (MRPT)

quaternions.ppt - 7a-Quaternions_Orientation.pdf

Tutorial 17 : Rotations

Free tutorials for modern Opengl (3.3 and later) in C/C++

quaternions-cs520.ppt - quaternions-cs520.pdf

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Week 9, Wed 29 Oct 03 © Tamara Munzner3 Clipping analytically calculating the portions of primitives within the viewport

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Quaternions work fine whether I grasp them or not, but I'm just curious: what is the "w"?

Quaternion Quaternion (float x, float y, float z, float...

Self Localisation? - ROS Answers: Open Source Q&A Forum

Hi, I have AMCL working well - but setting the initial pose is annoying - is there a package that will set the likely pose of the robot based on is current samples of data relative to the map? Thanks Mark

Localization SLAM how to do? - ROS Answers: Open Source Q&A Forum

Hey guys, I try to develop an autonomous driving car. The problem is that I don't know how to localize the car on my SLAM map. I only use a LiDAR UTM-30LX LiDAR to navigate. I created successfully a SLAM map with hector mapping. Now I want to estimate a start point and a goal. I use the navigation stack with move base to navigate. As local planner I have implemented the teb_local_planner because of the Ackermann steering. I can estimate a start point and can set a goal. I also can forward my cmd_vel to the car with a steering angle and a speed info. But the position of the robot on the map in rviz don't change. Can you help me and tell me what I have to do? I can't start playing around to find a good config for the teb_local_planner like this :( Here is my launch file: <launch> <node pkg="urg_node" type="urg_node" name="urg_node"> <param name="ip_address" type="string" value="192.168.0.10"/> </node> <param name="pub_map_odom_transform" value="true"/> <param name="map_frame" value="map"/> <param name="base_frame" value="base_frame"/> <param name="odom_frame" value="odom"/> <node pkg="tf" type="static_transform_publisher" name="map_2_odom" args="0 0 0 0 0 0 /map /odom 100"/> <node pkg="tf" type="static_transform_publisher" name="odom_2_base_footprint" args="0 0 0 0 0 0 /odom /base_footprint 100"/> <node pkg="tf" type="static_transform_publisher" name="base_footprint_2_base_link" args="0 0 0 0 0 0 /base_footprint /base_link 100"/> <node pkg="tf" type="static_transform_publisher" name="base_link_2_base_stabilized_link" args="0 0 0 0 0 0 /base_link /base_stabilized 100"/> <node pkg="tf" type="static_transform_publisher" name="base_stablized_2_base_frame" args="0 0 0 0 0 0 /base_stabilized /base_frame 100"/> <node pkg="tf" type="static_transform_publisher" name="base_frame_2_laser_link" args="0 0 0 0 0 0 /base_frame /laser 100"/> <node pkg="tf" type="static_transform_publisher" name="base_2_nav_link" args="0 0 0 0 0 0 /base_frame /nav 100"/> <node pkg="amcl" type="amcl" name="amcl"/> <node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen"> <rosparam file="$(find autonomous_car)/launch/costmaps/costmap_common_params.yaml" command="load" ns="global_costmap"/> <rosparam file="$(find autonomous_car)/launch/costmaps/costmap_common_params.yaml" command="load" ns="local_costmap"/> <rosparam file="$(find autonomous_car)/launch/costmaps/local_costmap_params.yaml" command="load"/> <rosparam file="$(find autonomous_car)/launch/costmaps/global_costmap_params.yaml" command="load"/> <rosparam file="$(find autonomous_car)/launch/costmaps/teb_local_planner_params.yaml" command="load"/> <param name="clearing_rotation_allowed" value="false"/> </node> <node pkg="rviz" type="rviz" name="rviz" args="-d $(find hector_slam_example)/launch/rviz_cfg.rviz"/> </launch> My amcl_pose dont update, but the car is moving with the speed and angle from the first input :/ rostopic echo /amcl_pose header: seq: 0 stamp: secs: 1484760781 nsecs: 434161143 frame_id: map pose: pose: position: x: 1.50654940711 y: 3.46721180218 z: 0.0 orientation: x: 0.0 y: 0.0 z: -0.773900494384 w: 0.633307212016 covariance: [0.18227685925353398, 0.0027654181834790847, 0.0, 0.0, 0.0, 0.0, 0.0027654181834790847, 0.22445548719072939, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ... (more)

rf2o_laser_odometry - ROS Wiki

Integrating range sensor into AMCL package - ROS Answers: Open Source Q&A Forum

Hi all, I was planning on incorporating a range-sensor ( a scalar scalar measurement that represents the distance between the position of a known fixed beacon and the robot) into the AMCL package. As in, adding another resampling step, using weights solely based the range measurement, before or after resampling with the laser scanner. I was hoping someone could shed some light on how to do this properly since the AMCL code base is pretty big and long and I would prefer to do it right.. I noticed I can add news sensors (as it looks like there are some some .cpp and .h template files for sensors); but I don't see where and how I should add routines for weight calculations and resampling analogue to the odom and laser routines. Could someone point me to documentation on this or share their ideas on how to do this? Thanks for your time. Thomas

How amcl works? - ROS Answers: Open Source Q&A Forum

Hi! I use this file: <launch> <!-- Run the map server --> <node name="map_server" pkg="map_server" type="map_server" args="$(find robot_setup_tf)/mymaps/map.yaml" output="screen"/> <include file="$(find frontier_exploration)/launch/amcl_diff.launch"> </include> <node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen"> <rosparam file="$(find frontier_exploration)/launch/costmap_common_params.yaml" command="load" ns="global_costmap" /> <rosparam file="$(find frontier_exploration)/launch/costmap_common_params.yaml" command="load" ns="local_costmap" /> <rosparam file="$(find frontier_exploration)/launch/local_costmap_params.yaml" command="load" /> <rosparam file="$(find frontier_exploration)/launch/global_costmap_params.yaml" command="load" /> <rosparam file="$(find frontier_exploration)/launch/base_local_planner_params.yaml" command="load" /> </node> <node pkg="rviz" type="rviz" name="rviz" args="-d rviz_cfg.rviz"/> </launch> but what amcl would do? Because I see the map and what laser scan see but nothing happens. I think it should overlap the scan points on the map, is it correct? Thanks you!

GMapping Odometry Question - ROS Answers: Open Source Q&A Forum

Hello, I am running GMapping and visualizing the map in Rviz. My fixed frame is set to /map. I am driving the robot around manually and am generating a fairly accurate map. My problem is that the link from odom to base_link seems to be moving in the opposite direction. Base_link remains fixed where at the origin like I expect, but odom moves the opposite direction of the robot. My /odom ends up being no where near where the map is being generated, but the map still looks fine. My /odom is publishing that it is at X=3.28, Y=0.44, theta=-11.128 degrees. However, when I look at its position in RViz for the odom frame, I get X=-0.61441, Y=-0.22086.

AMCL acceptable robot speed - ROS Answers: Open Source Q&A Forum

I am using amcl. my robot speed is 40 cm/sec. AMCL probability estimation is good and But robot is losting on gmapping map. you think problem about speed value ??? my problem : ( I used 2d pose estimation button for initial pose )

how to create a 2D map from laser scan data - ROS Answers: Open Source Q&A Forum

Hi all, I'm new to ROS. I'm using ROS INDIGO and kinect for my project.I want to create a map from the laser scan data without having odometry data .I converted the depth image to laser scan data.Then how can I create a map using Gmapping for the hand held kinect using this laser scan data? edited question. i used hectorslam and there is some problem with tf. using rosrun tf view_frames i got and this is my laaunch file for depthimage_to_laserscan kinect.launch <launch> <node name="depthimage_to_laserscan" pkg="depthimage_to_laserscan" type="depthimage_to_laserscan"> <remap from="image" to="/camera/depth_registered/image_raw"/> </node> </launch>

how to set tf for gmapping - ROS Answers: Open Source Q&A Forum

Hi everyone, I am trying to build a map using gmapping with my robot in Gazebo,and I faced a problem about setting up the right tf so the gmapping can do the right job. Now I am able to load the urdf file into Gazebo, display it in rviz and observe the data of Laser scans. But when I use the command rosrun gmapping slam_gmapping,it says [ WARN] [1455451234.661271473, 384.435000000]: MessageFilter [target=odom ]: Dropped 100.00% of messages so far. Please turn the [ros.gmapping.message_notifier] rosconsole logger to DEBUG for more information. I suppose the tf or the odom is not set propelly,so I tried rosrun tf view_frames, and I get robot/odom→robot/base_footprint→base_footprint→base_link→hokuyo_link I have also checked the tutorials and books,but these mostly use packages like turtlebot etc. in a bag playback mode rather than real-time gmapping and I could not understand how to boardcast the right transform in this case. Can any one help on this issue? What are the exact things I need to do? Many thanks in advance!

An Evaluation of 2D SLAM Techniques Available in Robot Operating System (PDF Download Available)

Official Full-Text Publication: An Evaluation of 2D SLAM Techniques Available in Robot Operating System on ResearchGate, the professional network for scientists.

ethzasl_icp_mapper - ROS Wiki

GuirguisMEThesis10.pdf

uniklinik_global_relations_index_count.eps - kuemmerle09auro.pdf

MIT Stata Center Data Set

bfl/Tutorials/Example of using a particle filter for localization by bfl library - ROS Wiki

Ubuntu 15.04 (Vivid Vervet)

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kinetic/Installation/Ubuntu - ROS Wiki

MIRA: Comparison of MIRA and ROS

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Figure out which platform is right for the project you want to do: Arduino Uno vs BeagleBone vs Raspberry Pi. Each is great in its own way.

Beaglebone Black: Ubuntu 14.04 + ROS (Indigo) install – Flesh and machines

I have started to update my old projects with Beaglebone Black and ROS Indigo version. Before ROS installation the best option is to install the Ubuntu 14.04(LTS) arm version. It is recommended because package install will be available for ROS and there's no need to build the them. Ubuntu Arm 14.04 Install I had chosen…

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2d SLAM with ROS and Kinect – Dr Rainer Hessmer

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A Gentle Introduction to ROS - Jason M. O'Kane

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Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods: Juan-Antonio Fernández-Madrigal, José Luis Blanco Claraco: 9781466621046: Amazon.com: Books

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Object recognition for robots | MIT News

MIT computer scientists have demonstrated how simultaneous localization and mapping (SLAM) can be used to improve object-recognition systems — a vital component of future robots that manipulate the objects around them in arbitrary ways.

3D SLAM without IMU possible? - ROS Answers: Open Source Q&A Forum

hi, I'm very new to ros and working on the same problem of building 3d map using lidar but i don't have imu. can you please tell me is it possible to build 3d map without imu? and for that what is the procedure. I have pioneer 3at robot with ros jade version.

How to perform SLAM without a laser scanner or a Kinect - Quora

ROS-Users - SLAM pretty much requires laser?

SLAM pretty much requires laser?. Hi, Several people in Trossen Robotics community are just starting to use ROS. Our robots are usually smaller and cheaper. Hokuyo and SICK sensors are way out of...

Simultaneous localization and mapping with limited sensing

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Developing 2D and 3D Tracking/SLAM Projects in 2017 | Kudan

Kudan’s CTO John Williams replies to some questions about their work on 2D tracking, and the exciting new developments in the area of 3D tracking and SLAM.

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM (PDF Download Available)

Official Full-Text Publication: A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM on ResearchGate, the professional network for scientists.

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2_building_nodes_&_turtlebots.pdf

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ROS support for iRobot Create 2? - ROS Answers: Open Source Q&A Forum

Hi, I just got an iRobot Create 2, see the link http://www.irobot.com/About-iRobot/ST... I tried to use ROS on it; soon, I find it is not that straightforward. Here are what I did so far. I installed ROS Indigo on a virtual machine with Ubuntu 14.04. Then, I looked for the package related to this iRobot Create 2 on the ROS.org , this is what I found http://www.ros.org/browse/search.php?... Then, I installed the create_driver in my Indigo ROS. I think this driver is for iRobot Create not iRobot Create 2. Here, I want to ask for help. Does anyone here have some experience on how to make iRobot Create 2 move via ROS Indigo? Thanks! If you are interested or also stuck as me, we may team up to figure this out.

mobile robot - SLAM with iRobot Create 2 - Robotics Stack Exchange

How to proceed with this SLAM implementation on an iRobot Create with Kinect? - ROS Answers: Open Source Q&A Forum

I am not referring to a turtlebot. I have an iRobot Create (old one) and a Kinect connected to it. I am trying to use the kinect to generate a 2D map of the robot environment and later navigate the robot autonomously through it. I am new to ROS and was wondering if there are any tutorials that someone could suggest that describe a step by step process? If not, then can someone help me with defining an outline for how to proceed? Till now I have this - A netbook with ROS Groovy running Installed the ros-brown-driver package for iCreate Currently installed the Turtlebot packages as well. I am not sure how to proceed next. This is a very beginner question so I was hoping someone could help me out?

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    <DT><A HREF="http://www.mrpt.org/" ADD_DATE="1490170525" LAST_MODIFIED="1490170525" LAST_CHARSET="UTF-8">MRPT – Empowering C++ development in robotics</A>
    <DD>
    <DT><A HREF="http://www.mrpt.org/tutorials/supported_hardware_and_sensors/#25_RoboPeak_RP-LIDAR_laserScanners" ADD_DATE="1490170649" LAST_MODIFIED="1490170649" LAST_CHARSET="UTF-8">Supported hardware and sensors – MRPT</A>
    <DD>
    <DT><A HREF="http://ieeexplore.ieee.org/document/7451592/" ADD_DATE="1490170747" LAST_MODIFIED="1490170747" ICON_URI="http://ieeexplore.ieee.org/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">A mapping mobile robot using RP Lidar scanner - IEEE Xplore Document</A>
    <DD>The environment mapping in one of the necessary aspects in mobile robotics studies when dealing with localization, positioning, autonomous navigation, as w
    <DT><A HREF="http://wiki.ros.org/rplidar" ADD_DATE="1490170841" LAST_MODIFIED="1490170841" ICON_URI="http://wiki.ros.org/custom/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">rplidar - ROS Wiki</A>
    <DD>
    <DT><A HREF="http://www.mrpt.org/category/publications/" ADD_DATE="1490171198" LAST_MODIFIED="1490171198" LAST_CHARSET="UTF-8">Publications – MRPT</A>
    <DD>
    <DT><A HREF="http://ingmec.ual.es/~jlblanco/papers/" ADD_DATE="1490171583" LAST_MODIFIED="1490171583" LAST_CHARSET="UTF-8">Index of /~jlblanco/papers</A>
    <DD>
    <DT><A HREF="http://home.isr.uc.pt/~davidbsp/publications/" ADD_DATE="1490173078" LAST_MODIFIED="1490173078" LAST_CHARSET="UTF-8">Index of /~davidbsp/publications</A>
    <DD>
    <DT><A HREF="http://www.lucacarlone.com/index.php/resources/research/lago" ADD_DATE="1490173578" LAST_MODIFIED="1490173578" ICON_URI="http://www.lucacarlone.com/templates/gratis/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Lago2D</A>
    <DD>
    <DT><A HREF="http://wiki.ros.org/ethzasl_sensor_fusion" ADD_DATE="1490175784" LAST_MODIFIED="1490175784" ICON_URI="http://wiki.ros.org/custom/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">ethzasl_sensor_fusion - ROS Wiki</A>
    <DD>
    <DT><A HREF="http://answers.ros.org/question/30376/hector_localization/" ADD_DATE="1490176023" LAST_MODIFIED="1490176023" ICON_URI="http://answers.ros.org/m/ros/media/images/ros_favicon.gif?v=27" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">hector_localization - ROS Answers: Open Source Q&amp;A Forum</A>
    <DD>Hi, if anyone could help i&#39;d be grateful. I chose that topic, but if anyone knows about any other package/stack that could solve my problem, shoot!. :D I am looking for a package that estimates a robot&#39;s position over time, using only the information of an IMU.(if there is one...) I probably found one(hector_localization) that can solve my problem, however I cant make it work. It compiled without any problem, I changed the subscribed topic to have the same name as the one published by my lse_xsens. I can get the orientations but not the position. I dont know if it needs the information of gps or any other sensor to work. I found no information explaining how to use, so I&#39;d appreciate any help I can get. Thanks.
    <DT><A HREF="https://research.google.com/pubs/pub45466.html" ADD_DATE="1490176516" LAST_MODIFIED="1490176516" ICON_URI="https://research.google.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Real-Time Loop Closure in 2D LIDAR SLAM</A>
    <DD>
    <DT><A HREF="https://github.com/mikeferguson/albany-ros-pkg/tree/master/slam_coreslam/coreslam" ADD_DATE="1490178158" LAST_MODIFIED="1490178158" ICON_URI="https://assets-cdn.github.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">albany-ros-pkg/slam_coreslam/coreslam at master · mikeferguson/albany-ros-pkg · GitHub</A>
    <DD>Automatically exported from code.google.com/p/albany-ros-pkg
    <DT><A HREF="https://www.gumstix.com/tutorial/tutorial-part-1-gumstix-and-the-irobot-create-2/" ADD_DATE="1490255507" LAST_MODIFIED="1490255507" ICON_URI="https://www.gumstix.com/wp-content/themes/gumstix-magento/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Tutorial: Part 1 - Gumstix and the iRobot Create 2 | Gumstix, Inc. | Gumstix, Inc.</A>
    <DD>
    <DT><A HREF="http://www.jetsonhacks.com/tag/irobot-create-2/" ADD_DATE="1490255672" LAST_MODIFIED="1490255672" LAST_CHARSET="UTF-8">iRobot Create 2 Archives - JetsonHacks</A>
    <DD>
    <DT><A HREF="http://www.jetsonhacks.com/about-us/" ADD_DATE="1490255791" LAST_MODIFIED="1490255791" LAST_CHARSET="UTF-8">About - JetsonHacks</A>
    <DD>JetsonHacks is a site devoted to developing on the NVIDIA Jetson Development Kits.
    <DT><A HREF="https://www.udacity.com/course/machine-learning-supervised-learning--ud675" ADD_DATE="1490858266" LAST_MODIFIED="1490858266" ICON_URI="https://www.udacity.com/favicon.ico?b89119ce0a1f3b4649cdd1d9d9fb3273" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Machine Learning Course On Supervised Learning - Udacity</A>
    <DD>Learn how to apply Supervised Learning techniques to solve a range of data science problems. Start Machine Learning today!
    <DT><A HREF="https://www.tertiaryrobotics.com/" ADD_DATE="1490861120" LAST_MODIFIED="1490861120" ICON_URI="https://www.tertiaryrobotics.com/pub/media/favicon/stores/1/favicon.png" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Singapore Robotics Store - Arduino, Raspberry Pi, PCduino, 3D Printers, 3D Scanners | Tertiary Robotics Store</A>
    <DD>Tertiary Robotics Store is a Singapore based online store for arduino, raspberry pi, robotics and electronics products.
    <DT><A HREF="http://www.sustainablenetworks.org/CIS623/?page_id=142" ADD_DATE="1491285834" LAST_MODIFIED="1491285834" ICON_URI="http://www.sustainablenetworks.org/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Lab 2.3: Setup GPS and Inertial Measurement Unit (IMU) modules</A>
    <DD>
    <DT><A HREF="https://ubuntuforums.org/showthread.php?t=2238087&page=2" ADD_DATE="1493790755" LAST_MODIFIED="1493790755" ICON_URI="https://ubuntuforums.org/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="windows-1252">[SOLVED] 14.04 Wireless internet connection not working, (Laptop Lenovo G40-30) - Page 2</A>
    <DD>Hi 

I have a laptop Lenovo G40-30, it already had windows 8.1 and I made a partition and I installed Ubuntu 14.04, my wireless connection hasn't work in ubuntu since there, (in windows is working fine)

I also checked http://ubuntuforums.org/showthread.php?t=2214110 for installing a Broadcom, but my pci.id does not show up in the list.

The code generated by the wireless_script (as suggested by http://ubuntuforums.org/showthread.php?t=370108) is below, please help

##########

[SOLVED] wifi not working on my new lenovo thinkpad x-1 - Linux Mint Forums

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Hi everyone, I'm new in this world of mobile robots and ROS. Currently I wanna know if there are techniques implemented in ROS for self-localization or localization of a robot in a known map that it's provided by a map_server. I try to use AMCL from de Navigation Stack but I could not make it work because I use hector_mapping without odometry information to build a accurate map and, in this moment, my robot doesn't provide me the odometry information. I just wanna know if anyone in this community has/meets or has developed a technique ir ros to do this requirement for navigating. I configured my amcl.launch like several examples in this community but AMCL doesn't work. I tried this solution ( http://answers.ros.org/question/10497... ) and unfortunately I could not get good results, in RVIZ the particleCloud topic doesn't provide a real good localization, any error or warning doesn't appear in the console and all particles, provide in this topic, diverge against to converge to a good pose. I don't know what to do. I think I have to provide a good odometry from my robot but I have read that if I provide bad odometry, AMCL won't work. Anyone knows or has a good tutorial to do that, the information from AMCL package is still insufficient for me because I'm a noob in these topics. Please, help me, I would appreciate any idea, comment or any solution to do this.

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        <DT><A HREF="http://download.eclipse.org/umlgen/updates/milestones/1.0.0rc5/mars/" ADD_DATE="1459935066" LAST_MODIFIED="1459935066" ICON_URI="http://download.eclipse.org/eclipse.org-common/themes/solstice/public/images/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Eclipse software repository</A>
        <DD>
        <DT><A HREF="http://research.ijcaonline.org/volume63/number21/pxc3885585.pdf" ADD_DATE="1470292317" LAST_MODIFIED="1470292317" LAST_CHARSET="UTF-8">Counting Objects using Homogeneous Connected Components - pxc3885585.pdf</A>
        <DD>
        <DT><A HREF="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934246/" ADD_DATE="1472521487" LAST_MODIFIED="1472521487" ICON_URI="http://www.ncbi.nlm.nih.gov/favicon.ico" ICON="data:image/png;base64,AAABAAEAEBAQAAEABAAoAQAAFgAAACgAAAAQAAAAIAAAAAEABAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAABkQiQAtKKUAOTa1ACMclwAyL60AHRaPAD08vQAnIp0ANzOxACEZkwA7ObkAKSWhABsTiwAvK6cAJR+ZAD8/vwA/////yj/////////MANG//////8TkAUv//+tj//20B//8Xb///9sT/8Z////+uT//+AS////////JQADO0r/////R8AAAJT/////9ogewE//+uj///9lf/9Fb////yWv/zVv///9Fv//EAMRL4b////4MAA////////2QU////8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" LAST_CHARSET="UTF-8">Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison</A>
        <DD>
        <DT><A HREF="http://www.flir.com/uploadedFiles/CS_EMEA/Application_Stories/Media/Downloads/BMW_A_EN.pdf" ADD_DATE="1472525185" LAST_MODIFIED="1472525185" ICON_URI="http://www.flir.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">BMW_A_EN.pdf</A>
        <DD>
        <DT><A HREF="http://possibility.teledynedalsa.com/the-long-wavelength-infrared-lwir-camera-market-heats-up/" ADD_DATE="1472528861" LAST_MODIFIED="1472528861" LAST_CHARSET="UTF-8">The Long Wavelength Infrared (LWIR) camera market heats up - Teledyne DALSA</A>
        <DD>Long Wavelength Infrared Cameras are being used in increasingly interesting ways
        <DT><A HREF="http://www.laserfocusworld.com/articles/print/volume-43/issue-4/features/long-wave-infrared-detectors-inspired-by-nature-ir-detector-targets-long-wavelength-applications.html" ADD_DATE="1472528953" LAST_MODIFIED="1472528953" ICON_URI="http://www.laserfocusworld.com/favicon.ico" ICON="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABOklEQVQ4je2SPYvCUBBF38/URitbsXmICJJOSSG8QklACShB06jYGJBYKPjRqGCITbALAZEUliI5Wyy7IKz1wrKnmWIud+AwQvwjeEMcxziOQ7PZpFAokMvl0HWdKIpeciIIAk6nE/f7/WXxfD65Xq/MZjN2ux3L5RLLsmg0Griui+/7hGGIUEpRKpXQdZ0gCNjv95zPZ8Iw5HA4IKWk3+9jWRbD4ZB6vU4mk6FcLjMajRDdbpdWq4WU8nuapkmxWEQpRT6fR9M0qtUqlUoFwzBQSlGr1YiiCNHpdNhsNqzXaxaLBePxGNd1abfbzOdzer0ex+MR0zTJZrOsVium0ylKqc+CdxK/uN1uDAYDpJR4nkeapq8SAR6PB3Ecc7lc8H2f7XaL53nYto1hGEwmE5Ik+fHAb3/Rn+ADxyVuT6hxyScAAAAASUVORK5CYII=" LAST_CHARSET="UTF-8">Long-wave infrared detectors: Inspired by nature, IR detector targets long-wavelength applications - Laser Focus World</A>
        <DD>The infrared spectrum has always offered possibilities beyond human visual capabilities.
        <DT><A HREF="http://roboticsandautomationnews.com/2016/07/27/panasonic-releases-improved-grid-eye-infrared-array-sensors/6272/" ADD_DATE="1472630937" LAST_MODIFIED="1472630937" ICON_URI="http://roboticsandautomationnews.com/wp-content/uploads/2015/09/cropped-ra-website-icon-192x192.jpg" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Panasonic releases ‘improved’ Grid-Eye infrared array sensors | Robotics &amp; Automation News</A>
        <DD>
        <DT><A HREF="https://www.renesas.com/en-in/software/D6000241.html" ADD_DATE="1473056062" LAST_MODIFIED="1473056062" ICON_URI="https://www.renesas.com/etc/designs/www/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Download Detail Page | Renesas Electronics India</A>
        <DD>
        <DT><A HREF="https://www.renesas.com/en-in/products/software-tools/software-os-middleware-driver/communication-software/m3s-t4-tiny.html" ADD_DATE="1473056070" LAST_MODIFIED="1473056070" ICON_URI="https://www.renesas.com/etc/designs/www/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">M3S-T4-Tiny | Renesas Electronics India</A>
        <DD>Renesas Software and Tools. TCP/IP protocol stack. High-Speed version. M3S-T4-Tiny.
        <DT><A HREF="https://www.renesas.com/en-in/products/microcontrollers-microprocessors/rx/rx600/rx63n-631.html#" ADD_DATE="1473056077" LAST_MODIFIED="1473056077" ICON_URI="https://www.renesas.com/etc/designs/www/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">RX63N, RX631 | Renesas Electronics India</A>
        <DD>The RX63N, 631 Group incorporates communication functions suitable for networking equipment, such as Ethernet controller, USB 2.0 full-speed (function, host, or OTG selectable), and CAN.

        <DT><A HREF="http://stackoverflow.com/questions/15297997/insertion-sort-descending-order" ADD_DATE="1473240094" LAST_MODIFIED="1473240094" ICON_URI="http://cdn.sstatic.net/Sites/stackoverflow/img/favicon.ico?v=4f32ecc8f43d" ICON="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABHElEQVQ4jZ1QLU8DQRDdhJSZURU1mFMVlXWIJrUklQgkrqm4y+5MVR0C1z9BmpAK+AHnMKfwJPyAUw3Jfcz8gSLgcNdueMmInZ335s1zLgJVSknMXC9MsGgDLv8vwDAxpkPrYRxNagMuVWDRvTVApkzvr3fuIm5roJkKliaw7UjKmCvjQ7z1zI2UMTfBokop+UrdlTEd1A+u+617GKvgzgLN/npMm98MbppweauCj70C9coNlWFtjJ/G+KEBsnrlhrXHuQqWxjCJPqH2OFfGvTI1Krhr/GB6/m7BQhn3LdNGBRZVSslPFrCOEmj8YKoB701gq4y5CpbK1Jjg20ni0/PLsddV5kZd8r1z3YcJHfsqSuAcTgrEVsyiaHwDeSy1MkMyrCcAAAAASUVORK5CYII=" LAST_CHARSET="UTF-8">java - Insertion sort - Descending order - Stack Overflow</A>
        <DD>
        <DT><A HREF="https://codeatnode.wordpress.com/2007/02/01/insertion-sort/" ADD_DATE="1473312929" LAST_MODIFIED="1473312929" ICON_URI="https://s2.wp.com/i/favicon.ico" ICON="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAACwElEQVRYhc1XQYrqQBDtgRzDS3gKL+BlcoocwJ2oWbtzHb1AwEWRhSCIiwSUgAR6Ed5fmMqvrnQb/fMHpqEX46SrXr1XVV1tjDFfb+/lcWY2lJg17c2KTlFatFFatGZFJ7OmvdlQYpbH2Uc2Rz9Y5BOzoYSdvbvNhhKzyCffA6AcT3dnxHmFrGxAtcXNtrjZFlRbZGWDOK8w3Z0xAPIxgEU+MWvas5H54QqqLQDgZltkZYOE7ojzCnFeIaE7tpcHbrYFAFBtMT9ceyBmTfsXbHicd/pOd2dkZQMAyMrGMRra88PVOcOMmBWdAiDCzpnidxzrPd2de5lGQIg/Otqnu3MfwaeONQiWpQexpr0fQJdwfOh/Oufc6UG4ifmkng9yhn/HeZQW7fbygF4yKCHF3+jnh2uPljcvPsi/yW/4tziv8AoAgD6fBAvmiw9xqckljUp65brZdvANByMX1dZh9wlgeZxJowndg7TJrcHqBsRy6mDkt8+23dEf51UfiaRe0i83G+OV0H2UKbbNrD5l6EovKxtsLw9EadFqFnwyRGnhoAwlLjPF/8/Kpmf1WZJd46Ha9lFo5CEZNMU+GTgZ2XZC9x6MWdHJyOyWkWqN/1UGZpPBSamjtGiDALRxnwy61Hwy8CUl7ToAfBL4NPbJAAAahJZB2x1I4EvCUITSON96ut6lM84leW6YhJ4yDDUT3em4q8mylTLEeTWQZViGqhHpq1calzJIsLpsOWIeXHRAbiNSrVgj9hmfH66OXLps2amP/mEr9lxGkgVtPM4rSPp9ZctDjHSmbTuXkb6OdS5I4zzljPUEqq2TM3rGcK9jz0Ai0WsZdLX4ylbnwuuBZGQk0zKEZkRZtrL3O4kXHMlGhlI25KPfV7bcAT8bSl+M5VyOIfpDZfvZWP7mw2R7efzgw+RXPM1+zeNU7x94nv8BTUzW1ysAG1cAAAAASUVORK5CYII=" LAST_CHARSET="UTF-8">Insertion Sort « Code@Node</A>
        <DD>Insertion sort is very simple sorting algorithm. It is an efficient algorithm to sort small number of elements. Insertion sort works the way many people sort a hand of playing card.We then remove one card at a time from the table and insert it into the correct position in the left hand. To find the…
        <DT><A HREF="http://www.nxp.com/files/microcontrollers/doc/app_note/AN3828.pdf" ADD_DATE="1474261766" LAST_MODIFIED="1474261766" ICON_URI="http://www.nxp.com/favicon.ico" ICON="data:image/png;base64,AAABAAEAEBAAAAEAIABoBAAAFgAAACgAAAAQAAAAIAAAAAEAIAAAAAAAAAAAABMLAAATCwAAAAAAAAAAAAAAtfn/ALX5/wC1+f/bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe/8A0sn/ANLJ/wDSyf8A0sn/ALX5/wC1+f8Atfn/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/ANLJ/wDSyf8A0sn/ANLJ/wC1+f8Atfn/ALX5/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/wDSyf8A0sn/ANLJ/wDSyf8Atfn/ALX5/wC1+f/bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe/8A0sn/ANLJ/wDSyf8A0sn/ALX5/wC1+f8Atfn/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/ANLJ/wDSyf8A0sn/ANLJ/wC1+f8Atfn/ALX5/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/wDSyf8A0sn/ANLJ/wDSyf8Atfn/ALX5/wC1+f/bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe/8A0sn/ANLJ/wDSyf8A0sn/ALX5/wC1+f8Atfn/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/ANLJ/wDSyf8A0sn/ANLJ/wC1+f8Atfn/ALX5/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/wDSyf8A0sn/ANLJ/wDSyf8Atfn/ALX5/wC1+f/bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe/8A0sn/ANLJ/wDSyf8A0sn/ALX5/wC1+f8Atfn/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/ANLJ/wDSyf8A0sn/ANLJ/wC1+f8Atfn/ALX5/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/wDSyf8A0sn/ANLJ/wDSyf8Atfn/ALX5/wC1+f/bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe/8A0sn/ANLJ/wDSyf8A0sn/ALX5/wC1+f8Atfn/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/ANLJ/wDSyf8A0sn/ANLJ/wC1+f8Atfn/ALX5/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/wDSyf8A0sn/ANLJ/wDSyf8Atfn/ALX5/wC1+f/bsXv/27F7/9uxe//bsXv/27F7/9uxe//bsXv/27F7/9uxe/8A0sn/ANLJ/wDSyf8A0sn/AAA6AAAAOgAAAEnxAADp6wAA2zwAAD6ZAAANHAAAR94AAE3IAACLpgAAWn0AACUfAADL/AAARTsAAIkKAACuMg==" LAST_CHARSET="UTF-8">Stepper Motor Motion Control Driver for MC9S08LG32 - AN3828.pdf</A>
        <DD>
        <DT><A HREF="http://www.futurlec.com/" ADD_DATE="1475137096" LAST_MODIFIED="1475137096" LAST_CHARSET="UTF-8">Futurlec, The Electronic Components and Semiconductor Superstore</A>
        <DD>The Electronic Components and Semiconductor Superstore, we carry a huge range of electronic components and electronic parts
        <DT><A HREF="http://sqi.org.sg/program_edp.html" ADD_DATE="1476065779" LAST_MODIFIED="1476065779" LAST_CHARSET="UTF-8">SQI Programs - Executive Development Programs</A>
        <DD>
        <DT><H3 ADD_DATE="1439530605" LAST_MODIFIED="1469010687">SensorProject</H3>
        <DL><p>
            <DT><A HREF="http://www.amazon.com/GeauxRobot-Raspberry-Case-Enclosure-Shape/dp/B00MRKSGP2/ref=pd_sim_147_10?ie=UTF8&refRID=0WFMHQ7PP00XMBDZTV9B" ADD_DATE="1439530598" LAST_MODIFIED="1439530598" ICON_URI="http://www.amazon.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Amazon.com: GeauxRobot Raspberry Pi B+ Case Box Enclosure Dog Bone Shape: Electronics</A>
            <DD>Amazon.com: GeauxRobot Raspberry Pi B+ Case Box Enclosure Dog Bone Shape: Electronics
            <DT><A HREF="http://www.amazon.com/Phantom-dupont-cable-200mm-female/dp/B00A6SOGC4/ref=pd_sim_23_20?ie=UTF8&refRID=1GMJZ49BXN3N2G82N3CK" ADD_DATE="1439530623" LAST_MODIFIED="1439530623" ICON_URI="http://www.amazon.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">Amazon.com: Phantom YoYo 40P dupont cable 200mm male to female: Electronics</A>
            <DD>Amazon.com: Phantom YoYo 40P dupont cable 200mm male to female: Electronics
            <DT><A HREF="http://www.amazon.com/40pcs-Female-2-54mm-Jumper-2x40pcs/dp/B00GSE2S98/ref=pd_sim_328_4?ie=UTF8&refRID=04JASD5AJP66G0MHPR64" ADD_DATE="1439530642" LAST_MODIFIED="1439530642" ICON_URI="http://www.amazon.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">2x 40pcs Female to Female 2.54mm 0.1 in Jumper Wires F/F (2x40pcs): Tools Products: Amazon.com: Computers &amp; Accessories</A>
            <DD>2x 40pcs Female to Female 2.54mm 0.1 in Jumper Wires F/F (2x40pcs): Tools Products: Amazon.com: Computers &amp; Accessories
            <DT><A HREF="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.3329&rep=rep1&type=pdf" ADD_DATE="1439537718" LAST_MODIFIED="1439537718" ICON_URI="http://citeseerx.ist.psu.edu/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">double-spaced.Italy.paper[1].fm - download</A>
            <DD>
            <DT><A HREF="http://www.oki.com/en/otr/2012/n219/pdf/otr-219-R15.pdf" ADD_DATE="1439537786" LAST_MODIFIED="1439537786" ICON_URI="http://www.oki.com/favicon.ico" ICON="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAwklEQVQ4je2RMQrCQBBFF/EEaUxhpkwtgeiyBBIkzYJayJY29h7AQ3iMnNIk+yw2GlPbCDowMMX/jz8zig9L/QEB0GrNPU3pkgSKIvSzRGCzGWcRAPqyhChCsV7TVxVd0wDg8xyf53C9Tk1ZBiIvXb/bwXKJYjbDWzuJ5bMM9vsREMcwn081xyOIoHCOvq7HBMbgjYHbbQSsVnC5wPmMr+ugcw7ieDji4UC73YZdrQ39foPBhAhoHebTCRaLb3njjwMeF7SlRgJM31IAAAAASUVORK5CYII=" LAST_CHARSET="UTF-8">otr-219-R15.pdf</A>
            <DD>
            <DT><A HREF="http://www.oecd.org/sti/44379113.pdf" ADD_DATE="1439537874" LAST_MODIFIED="1439537874" LAST_CHARSET="UTF-8">44379113.pdf</A>
            <DD>
            <DT><A HREF="https://www.edx.org/sites/default/files/course/image/promoted/artificial-intelligence-378x225.png" ADD_DATE="1439541331" LAST_MODIFIED="1439541331" LAST_CHARSET="UTF-8">artificial-intelligence-378x225.png (PNG Image, 378 × 225 pixels)</A>
            <DD>
            <DT><A HREF="http://na.industrial.panasonic.com/sites/default/pidsa/files/downloads/files/panasonic_industrial_line_card_catalog.pdf" ADD_DATE="1439541442" LAST_MODIFIED="1439541442" ICON_URI="http://na.industrial.panasonic.com/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">panasonic_industrial_line_card_catalog.pdf</A>
            <DD>
            <DT><A HREF="http://www.economymufflerandbrake.com/index.html" ADD_DATE="1439545772" LAST_MODIFIED="1439545772" LAST_CHARSET="UTF-8">Economy Muffler &amp; Brake</A>
            <DD>
            <DT><A HREF="https://www.google.com.sg/search?q=medical+elderly+monitoring&biw=1280&bih=631&source=lnms&tbm=isch&sa=X&ved=0CAYQ_AUoAWoVChMInvuKmaioxwIVJS2mCh12sQHj#tbm=isch&q=nurse+alert+system&imgrc=X4PJTQXCrYg_hM%3A" ADD_DATE="1439546811" LAST_MODIFIED="1439546811" ICON_URI="https://www.google.com.sg/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">nurse alert system - Google Search</A>
            <DD>
            <DT><A HREF="http://www.adamsmith.org/blog/regulation-industry/rds-great-but-why-a-target-for-spending-on-it/" ADD_DATE="1439548482" LAST_MODIFIED="1439548482" ICON_URI="http://www.adamsmith.org/wp-content/themes/asi/favicon.ico" ICON="data:image/png;base64,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" LAST_CHARSET="UTF-8">R&amp;D’s great but why a target for spending on it? « Adam Smith Institute</A>
            <DD>
            <DT><A HREF="https://www.executiveboard.com/blogs/6-myths-of-corporate-rd/" ADD_DATE="1439548630" LAST_MODIFIED="1439548630" ICON_URI="https://www.executiveboard.com/blogs/wp-content/themes/cebblogs/images/favicon.ico" ICON="data:image/png;base64,AAABAAEAEBAQAAAAAAAoAQAAFgAAACgAAAAQAAAAIAAAAAEABAAAAAAAgAAAAAAAAAAAAAAAEAAAAAAAAADvrgAA////APbUeAD77cYA8LQTAPC2GgD+/vsA8bghAP7+/gDxvC0A8bkkAO+wCQD88dMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEREREREAAAAREREREQAAAEsEAEC0AAAAEgEAECEAAAASAQAQIQAAABIBABAhAAAAAAAAAAAAAAAYAAAAYQAAABERlxERAAAAADERHAAAAAAAAFoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" LAST_CHARSET="UTF