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.

翻译：从 RGB-D 图像进行频繁实时跟踪和绘图是许多机器人应用的重要工具,例如导航和操纵。最近推出的“方向截断的远程功能”是常规的TSDF的增强,显示了更连贯的地图和改善跟踪性能的潜力。在这项工作中,我们提出了从DTSDF 绘制深度和彩色图像的方法,使之成为固定跟踪器中常规TSDF的真正的滴入替代。我们评估了已建立数据集的算法,并观察我们的方法改善了跟踪性能,提高了地图图像的可重新使用性。此外,我们增加了色彩整合,显著改善了相邻表面的色调调。我们新设计的与框架对钥匙的光度误差相结合的比较方案进一步改进了跟踪结果。最后,我们引入了Sim3点对平面比较方案,用于在多传感器情景下精炼前台,并使用不同规模因素。