Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed Distance Field (TSDF) submaps and panoptic labels to hierarchically model the environment. The output map is able to maintain both voxel- and submap-level metric and semantic information. Two modules are presented to enhance the mapping efficiency and label consistency: (1) an inter-submaps label fusion strategy to eliminate duplicate points across submaps and (2) a conditional random field (CRF) based approach to enhance panoptic labels through object label comprehension and contextual information. We conducted experiments with two public datasets including indoor and outdoor scenarios. Our system performs comparably to state-of-the-art (SOTA) methods across geometry and label accuracy evaluation metrics. The experiment results highlight the effectiveness and scalability of our system, as it is capable of constructing precise geometry and maintaining consistent panoptic labels. Our code is publicly available at https://github.com/hutslib/DHP-Mapping.

翻译：地图为机器人提供关键的环境知识，使其能够有效执行交互任务。从地图中便捷地获取从抽象到详细的精确几何与语义概念，对于机器人做出明智且高效的决策至关重要。为全面建模环境并有效管理地图数据结构，我们提出DHP-Mapping——一种利用多个截断符号距离场（TSDF）子地图和全景标签对环境进行分层建模的密集建图系统。输出地图能够同时维护体素级和子地图级的度量与语义信息。我们提出了两个模块以提升建图效率与标签一致性：（1）子地图间标签融合策略，用于消除子地图间的重复点；（2）基于条件随机场（CRF）的方法，通过对象标签理解与上下文信息来增强全景标签。我们使用包含室内与室外场景的两个公开数据集进行了实验。在几何与标签精度评估指标上，本系统性能与最先进（SOTA）方法相当。实验结果表明了本系统的有效性与可扩展性，能够构建精确的几何结构并保持全景标签的一致性。我们的代码已开源，地址为https://github.com/hutslib/DHP-Mapping。