  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.

Computer Vision and Pattern Recognition;Robotics