Combining edge images and depth maps for robust visual odometry

Abstract

In this work, we propose a robust visual odometry system for RGBD sensors. The core of our method is a combination of edge images and depth maps for joint camera pose estimation. Edges are more stable under varying lighting conditions than raw intensity values and depth maps further add stability in poorly textured environments. This leads to higher accuracy and robustness in scenes, where feature-or photoconsistency-based approaches often fail. We demonstrate the robustness of our method under challenging conditions on various real-world scenarios recorded with our own RGBD sensor. Further, we evaluate on several sequences from standard benchmark datasets covering a wide variety of scenes and camera motions. The results show that our method performs best in terms of trajectory accuracy for most of the sequences indicating that the chosen combination of edge and depth terms in the cost function is suitable for a multitude of scenes.