  Dense real-time tracking and mapping from RGB-D images is an important tool
for many robotic applications, such as navigation and manipulation. The
recently presented Directional Truncated Signed Distance Function (DTSDF) is an
augmentation of the regular TSDF that shows potential for more coherent maps
and improved tracking performance. In this work, we present methods for
rendering depth- and color images from the DTSDF, making it a true drop-in
replacement for the regular TSDF in established trackers. We evaluate the
algorithm on well-established datasets and observe that our method improves
tracking performance and increases re-usability of mapped scenes. Furthermore,
we add color integration which notably improves color-correctness at adjacent
surfaces. Our novel formulation of combined ICP with frame-to-keyframe
photometric error minimization further improves tracking results. Lastly, we
introduce Sim3 point-to-plane ICP for refining pose priors in a multi-sensor
scenario with different scale factors.
