  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.
