ICRA 2020: Beyond Photometric Consistency: Gradient-based Dissimilarity

ICRA 2020

Video presentation for ICRA 2020 paper Jan Quenzel, Radu Alexandru Rosu, Thomas Läbe, Cyrill Stachniss, and Sven Behnke: "Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching" In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020. http://www.ais.uni-bonn.de/papers/ICRA_2020_Quenzel.pdf Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-based metric with a magnitude-dependent scaling term. We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric. Our experimental evaluation indicates that our metric leads to more robust and more accurate estimates of the scene depth as well as camera trajectory. Thus, the metric improves camera pose estimation and in turn the mapping capabilities of mobile robots. We believe that a series of existing visual odometry and visual SLAM systems can benefit from the findings reported in this paper.