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：占据地图构建是机器人系统感知环境未知与已知区域的基本组成部分。本文针对高分辨率激光雷达传感器提出了一种高效的占据地图构建框架，命名为D-Map。该框架引入三项主要创新以解决占据地图构建的计算效率挑战。首先，我们使用深度图像而非传统射线投射方法来确定区域的占据状态。其次，我们在基于树结构的地图表示中引入了一种高效的树内更新策略。这两项技术避免了对小单元格的冗余访问，显著减少了需要更新的单元格数量。第三，我们利用激光雷达传感器的低虚警率特性，在每次更新时从地图中移除已知单元格。该方法不仅通过减小地图尺寸提升了框架的更新效率，还赋予了框架一种有趣的递减属性，我们据此将其命名为D-Map。为支持我们的设计，我们提供了深度图像投影精度的理论分析以及占据更新的时间复杂度分析。此外，我们在公开和私有数据集上使用多种激光雷达传感器进行了大量基准实验。与当前最先进方法相比，我们的框架在保持可比较的地图构建精度和高效内存使用的同时，展现了卓越的更新效率。我们展示了D-Map在手持设备和高分辨率激光雷达空中平台上的两项实时占据地图构建实际应用。同时，我们在GitHub上开源了D-Map的实现以惠及学界：github.com/hku-mars/D-Map。