SuperPoint-Visual Odometry (SP-VO)
Abstract
This Visual Odometry is a combination between monoVO-python and SuperPointPretrainedNetwork.
Requirements
Dataset
KITTI odometry data set (grayscale, 22 GB)
Pretrained model for SuperPoint
SuperPointPretrainedNetwork (superpoint_v1.pth)
Usage
Exec Visual Odometry
Modify the path and Sequence_Num in main.py to your image sequences and ground truth trajectories, then run
python main.py
VO Process is visualized in 2 screen (Trajectory and Feature extraction (up:SP-VO, down:Normal-VO))
Visualizer
Modify the path in result_visualizer.py to your output ("kitti_XX.txt")
python result_visualizer.py
Visualized example is following.
References
- monoVO-python
- SuperPointPretrainedNetwork
- Monocular Visual Odometry using OpenCV and its related project report Monocular Visual Odometry | Avi Singh