Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization
Computer Vision and Pattern Recognition
2021-10-22 v1 Robotics
Abstract
In this paper, we propose a robust edge-direct visual odometry (VO) based on CNN edge detection and Shi-Tomasi corner optimization. Four layers of pyramids were extracted from the image in the proposed method to reduce the motion error between frames. This solution used CNN edge detection and Shi-Tomasi corner optimization to extract information from the image. Then, the pose estimation is performed using the Levenberg-Marquardt (LM) algorithm and updating the keyframes. Our method was compared with the dense direct method, the improved direct method of Canny edge detection, and ORB-SLAM2 system on the RGB-D TUM benchmark. The experimental results indicate that our method achieves better robustness and accuracy.
Cite
@article{arxiv.2110.11064,
title = {Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization},
author = {Kengdong Lu and Jintao Cheng and Yubin Zhou and Juncan Deng and Rui Fan and Kaiqing Luo},
journal= {arXiv preprint arXiv:2110.11064},
year = {2021}
}