  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 indicats 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.
