As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT - an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application.

翻译：随着移动机器人应用场景日益复杂和具有挑战性，场景理解变得愈发关键。在室内环境中自主运行的移动机器人必须精确知道存在哪些物体、它们的位置、空间范围以及如何抵达——即自由空间的信息同样至关重要。全局感知建图正是提供此类信息的强大工具。然而，在移动机器人有限的计算能力下，构建高空间分辨率的3D全局感知地图极具挑战性。本文提出PanopticNDT——一种基于占据标准化正态分布变换建图的高效鲁棒全局感知方法。我们在公开数据集Hypersim和ScanNetV2上评估了该方法。结果表明，相较于其他前沿方法，我们的方法能以更高的细节层次表示全局感知信息，同时支持移动机器人的实时全局感知建图。最后，我们通过家庭场景应用的定性结果验证了PanopticNDT在现实世界中的实用性。