Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared with state-of-the-art handcrafted descriptors. As a consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.

中文翻译：

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SegMap：使用数据驱动描述符的基于段的映射和定位

在先前的全局地图中精确估计机器人的姿态是移动机器人的基本能力，例如自动驾驶或在灾区探索。然而，这项任务在非结构化的动态环境中仍然具有挑战性，在这些环境中，局部特征的辨别力不够，全局场景描述符仅提供粗略的信息。因此，我们提出了 SegMap：一种基于 3D 点云中段提取的定位和映射的地图表示解决方案。在分段级别上工作可提高视点和局部结构变化的不变性，并有助于大规模 3D 数据的实时处理。SegMap 利用单个紧凑的数据驱动描述符来执行多项任务：全局定位、3D 密集地图重建和语义信息提取。SegMap 的性能在多个城市驾驶和搜救实验中得到评估。我们表明，与最先进的手工描述符相比，学习的 SegMap 描述符具有优越的段检索能力。因此，与最先进的手工描述符相比，我们实现了更高的定位精度和 6% 的召回率增加。这些基于段的定位使我们能够将开环里程计漂移减少多达 50%。SegMap 是开源的，并且具有易于运行的演示。与最先进的手工描述符相比，我们实现了更高的定位精度和 6% 的召回率。这些基于段的定位使我们能够将开环里程计漂移减少多达 50%。SegMap 是开源的，并且具有易于运行的演示。与最先进的手工描述符相比，我们实现了更高的定位精度和 6% 的召回率。这些基于段的定位使我们能够将开环里程计漂移减少多达 50%。SegMap 是开源的，并且具有易于运行的演示。