Converting Depth Images and Point Clouds for Feature-based Pose
  Estimation
Robert Lösch Mark Sastuba Jonas Toth Bernhard Jung
In recent years, depth sensors have become more and more affordable and have
found their way into a growing amount of robotic systems. However, mono- or
multi-modal sensor registration, often a necessary step for further processing,
faces many challenges on raw depth images or point clouds. This paper presents
a method of converting depth data into images capable of visualizing spatial
details that are basically hidden in traditional depth images. After noise
removal, a neighborhood of points forms two normal vectors whose difference is
encoded into this new conversion. Compared to Bearing Angle images, our method
yields brighter, higher-contrast images with more visible contours and more
details. We tested feature-based pose estimation of both conversions in a
visual odometry task and RGB-D SLAM. For all tested features, AKAZE, ORB, SIFT,
and SURF, our new Flexion images yield better results than Bearing Angle images
and show great potential to bridge the gap between depth data and classical
computer vision. Source code is available here:
https://rlsch.github.io/depth-flexion-conversion.