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.
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.