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Dense real-time tracking and mapping from RGB-D images is an important toolfor many robotic applications, such as navigation and manipulation. Therecently presented Directional Truncated Signed Distance Function (DTSDF) is anaugmentation of the regular TSDF that shows potential for more coherent mapsand improved tracking performance. In this work, we present methods forrendering depth- and color images from the DTSDF, making it a true drop-inreplacement for the regular TSDF in established trackers. We evaluate thealgorithm on well-established datasets and observe that our method improvestracking performance and increases re-usability of mapped scenes. Furthermore,we add color integration which notably improves color-correctness at adjacentsurfaces. Our novel formulation of combined ICP with frame-to-keyframephotometric error minimization further improves tracking results. Lastly, weintroduce Sim3 point-to-plane ICP for refining pose priors in a multi-sensorscenario with different scale factors.

Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP