Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization
This work addresses visual odometry for robotics or autonomous systems, but it appears incremental as it builds on direct methods with CNN enhancements.
The paper tackles visual odometry by proposing a method using CNN edge detection and Shi-Tomasi corner optimization, achieving better robustness and accuracy compared to existing methods like dense direct, Canny edge detection, and ORB-SLAM2 on the RGB-D TUM benchmark.
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.