This project implements and evaluates Visual Odometry (VO) algorithms using Python in Jupyter notebooks. The project consists of two main components:

1. Robustness Evaluation of Visual Odometry - Testing VO performance under various image noise conditions
2. Visual Odometry on PuzzleBot - Implementation of VO on videos captured using a PuzzleBot robot

.
├── task1.ipynb          # Robustness evaluation notebook
├── task2.ipynb          # Camera calibration and PuzzleBot VO notebook
├── Python-VO/utils/               # Utility functions and classes
│   ├── PinholeCamera.py # Camera model implementation
│   └── tools.py         # Helper functions
├── Python-VO/Detectors/           # Feature detection implementations
│   └── superpoint/      # SuperPoint detector
├── Python-VO/Matchers/            # Feature matching implementations
│   └── superglue/       # SuperGlue matcher
├── calibration_images/  # Images for camera calibration
│   └── calibration_result.yaml # Calibration parameters
├── results/             # Output directory for results
└── README.md            # This file

This notebook evaluates how visual odometry performance degrades under different types and levels of image noise.

• images in KITTI loading and preprocessing
• Feature detection with SuperPoint
• Feature matching with SuperGlue
• Motion estimation using Essential matrix

• Gaussian Noise - Simulates electronic noise in imaging sensors
• Salt & Pepper Noise - Simulates dead pixels or transmission errors
• Speckle Noise - Simulates multiplicative noise common in radar imaging

• add_gaussian_noise() - Adds Gaussian noise to images
• add_salt_pepper_noise() - Adds salt and pepper noise to images
• add_speckle_noise() - Adds speckle (multiplicative) noise to images
• evaluate_vo_with_noise() - Evaluates VO performance with different noise parameters
• plot_results() - Visualizes trajectory comparison and error metrics
• plot_all_noise_comparisons() - Creates comprehensive visualizations
• generate_rmse_matrix() - Produces tabulated results
• run_all_evaluations() - Orchestrates the complete evaluation process

• Absolute RMSE - Overall trajectory error compared to ground truth
• Relative RMSE - Frame-to-frame motion error
• Processing Time - Computational efficiency under different noise conditions

This notebook implements visual odometry on videos captured using the PuzzleBot robot with Jetson Nano.

• Chessboard pattern detection
• Camera intrinsic parameter estimation
• Distortion coefficient calculation
• Undistortion visualization

• Video loading and preprocessing
• Feature detection with SuperPoint
• Feature matching with SuperGlue
• Motion estimation using Essential matrix
• Trajectory tracking and visualization

• read_calibration_yaml() - Loads camera calibration parameters
• run_vo_on_video() - Main function for running VO on video files
• TrajPlotter - Class for visualizing and saving trajectory data

first install the required packages:in requirments.txt then install the required model weights about the superpoint and superglue in: https://github.com/sherrywsy3446/Python-VO.git place the whole python-vo folder in the same directory as the notebooks

1. For Task 1 (Robustness Evaluation):

   • Open task1.ipynb in Jupyter Notebook
   • Run all cells to perform the complete evaluation
   • Adjust the num_frames parameter in the final cell to control evaluation length
   • Results will be saved in the results directory

2. For Task 2 (PuzzleBot VO):

   • Place calibration images in the calibration_images directory
   • Open task2.ipynb in Jupyter Notebook
   • Run the calibration section first to generate calibration parameters
   • Update the video path to point to your captured video
   • Run the VO implementation section
   • Visualizations and trajectory data will be saved in the results directory

• Deep learning-based feature detection and matching
• Robust camera calibration for accurate pose estimation
• Comprehensive noise simulation and evaluation
• Real-world testing on mobile robot platform
• Extensive visualization and analysis