This repository implements a search-based technique for 6D pose estimation of objects in clutter as described in our paper pdf.
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search.
By Chaitanya Mitash, Kostas Bekris, Abdeslam Boularias (Rutgers University).
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.
- Clone the repository.
- Download object models: Models
- Copy models to
$PHYSIM_GLOBAL_POSE/src/physim_pose_estimation/ - Download trained fcn weights: FCN Model
- Copy weights to
$PHYSIM_GLOBAL_POSE/src/3rdparty/fcn_segmentation_package - Download and extract Bullet
export BULLET_PHYSICS_PATH=/path/to/bullet/bullet3-2.86.1/
export PHYSIM_GLOBAL_POSE=/path/to/repo/PhysimGlobalPose
source $PHYSIM_GLOBAL_POSE/devel/setup.sh
cd $PHYSIM_GLOBAL_POSE/src
catkin_init_workspace
cd $PHYSIM_GLOBAL_POSE
catkin_make
rosrun physim_pose_estimation physim_pose_estimation
run $PHYSIM_GLOBAL_POSE/src/3rdparty/fcn_segmentation_package/predict
rosservice call /pose_estimation "APC" "$PHYSIM_GLOBAL_POSE/test-scene/" "FCNThreshold" "PCS" "LCP"
- Estimated 6D pose of all objects in the scene.
- Ubuntu 14.04/16.04
- Cuda 8.0, CudNN 5.0
To cite the work:
@inproceedings{mitash2017improving,
Author = {Mitash, Chaitanya and Boularias, Abdeslam and Bekris, Kostas E},
Booktitle = {{IEEE} International Conference on Robotics and Automation (ICRA)},
Title = {Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search},
Year = {2018}}