Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution LiDAR sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping. Firstly, we use a depth image to determine the occupancy state of regions instead of the traditional ray-casting method. Secondly, we introduce an efficient on-tree update strategy on a tree-based map structure. These two techniques avoid redundant visits to small cells, significantly reducing the number of cells to be updated. Thirdly, we remove known cells from the map at each update by leveraging the low false alarm rate of LiDAR sensors. This approach not only enhances our framework's update efficiency by reducing map size but also endows it with an interesting decremental property, which we have named D-Map. To support our design, we provide theoretical analyses of the accuracy of the depth image projection and time complexity of occupancy updates. Furthermore, we conduct extensive benchmark experiments on various LiDAR sensors in both public and private datasets. Our framework demonstrates superior efficiency in comparison with other state-of-the-art methods while maintaining comparable mapping accuracy and high memory efficiency. We demonstrate two real-world applications of D-Map for real-time occupancy mapping on a handle device and an aerial platform carrying a high-resolution LiDAR. In addition, we open-source the implementation of D-Map on GitHub to benefit society: github.com/hku-mars/D-Map.

翻译：占据建图是机器人系统感知环境未知与已知区域的基础技术。本文提出一种面向高分辨率激光雷达的高效占据建图框架D-Map。该框架针对占据建图的计算效率挑战引入三项主要创新：首先，采用深度图像替代传统射线投射法确定区域占据状态；其次，基于树形地图结构提出高效的树上更新策略。这两项技术避免了对小尺寸网格的冗余访问，显著减少了需要更新的网格数量。第三，利用激光雷达低虚警率的特性，在每次更新时移除地图中的已知网格。该方法不仅通过减小地图规模提升框架更新效率，更赋予其独特的递减特性——这也是D-Map命名的由来。为支撑设计方案，本文提供了深度图像投影精度的理论分析及占据更新的时间复杂度分析。进一步，我们在公开与私有数据集上使用多种激光雷达开展广泛基准实验。与现有先进方法相比，本框架在保持可比的建图精度和高内存效率的同时，展现出卓越的更新效率。我们展示了D-Map在手持设备与搭载高分辨率激光雷达的飞行平台上进行实时占据建图的两项实际应用。此外，已在GitHub开源D-Map的实现代码（github.com/hku-mars/D-Map）以回馈社会。