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图像中进行密集实时跟踪与建图是众多机器人应用（如导航与操作）的重要工具。近期提出的定向截断符号距离函数（DTSDF）是对常规TSDF的增强，展现出构建更连贯地图及提升跟踪性能的潜力。本文提出了从DTSDF中渲染深度与彩色图像的方法，使其成为现有跟踪器中常规TSDF的真正即插即用替代方案。我们基于成熟数据集对算法进行了评估，观察到该方法不仅提升了跟踪性能，还增强了已建图场景的可复用性。此外，我们加入了颜色整合功能，显著改善了相邻表面的颜色正确性。我们提出的组合ICP与帧到关键帧光度误差最小化的新公式进一步优化了跟踪结果。最后，我们引入了Sim3点面ICP算法，用于在多传感器场景中针对不同尺度因子精化位姿先验。