# D-BDM: A Direct and Efficient Boundary-Based Occupancy Grid Mapping Framework for LiDARs Source: [D-BDM: A Direct and Efficient Boundary-Based Occupancy Grid Mapping Framework for LiDARs]() Collection index: [llms.txt]() Data updated: 2026-04-16T05:00:04.684Z Authors: Benxu Tang, Yixi Cai, Fanze Kong, Longji Yin, Fu Zhang Published: 2026-04-14T08:27:03.000Z ## Abstract Efficient and scalable 3D occupancy mapping is essential for autonomous robot applications in unknown environments. However, traditional occupancy grid representations suffer from two fundamental limitations. First, explicitly storing all voxels in three-dimensional space leads to prohibitive memory consumption. Second, exhaustive ray casting incurs high update latency. A recent representation alleviate memory demands by maintaining only the voxels on the two-dimensional boundary, yet they still rely on full ray casting updates. This work advances the boundary-based framework with a highly efficient update scheme. We introduce a truncated ray casting strategy that restricts voxel traversal to the exterior of the boundary, which dramatically reduces the number of updated voxels. In addition, we propose a direct boundary update mechanism that removes the need for an auxiliary local 3D occupancy grid, further reducing memory usage and simplifying the map update pipeline. We name our framework as D-BDM. Extensive evaluations on public datasets demonstrate that our approach achieves significantly lower update time and reduced memory consumption compared with the baseline methods, as well as the prior boundary-based approach. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Occupancy Mapping on kitti 00 (Memory Consumption)](): Occupancy Mapping; Kitti 00; Memory Consumption (MB): 29.2. - [Occupancy Mapping on KITTI 02 (Memory Consumption)](): Occupancy Mapping; kitti 02; Memory Consumption (MB): 36.5. - [Occupancy Mapping on college](): Occupancy Mapping; College; Memory Consumption (MB): 7.4. - [Occupancy Mapping on KITTI 00 (Update Time)](): Occupancy Mapping; Kitti 00; Map Update Time (ms): 10.78. - [Occupancy Mapping on kitti 02 (Update Time)](): Occupancy Mapping; kitti 02; Map Update Time (ms): 10.92. - [Occupancy Mapping on college (Map Update Time (ms))](): Occupancy Mapping; College; Map Update Time (ms): 12.95. - [Occupancy Mapping on KAIST 04](): Occupancy Mapping; KAIST 04; Memory Consumption (MB): 38.9. - [Occupancy Mapping on Town 03](): Occupancy Mapping; Town 03; Memory Consumption (MB): 62.5. - [Occupancy Mapping on KAIST 04 (Map Update Time)](): Occupancy Mapping; KAIST 04; Map Update Time (ms): 10.8. - [Occupancy Mapping on Town 03 (Map Update Time)](): Occupancy Mapping; Town 03; Map Update Time (ms): 10.3. - [Occupancy Mapping on Roundabout 01](): Occupancy Mapping; Roundabout 01; Memory Consumption (MB): 65.6. - [Occupancy Mapping on Real-world MAV multi-floor building navigation](): Occupancy Mapping; Real-world MAV multi-floor building navigation; Map Update Time: 15.58. - [Occupancy Mapping on Roundabout 01 (Map Update Time)](): Occupancy Mapping; Roundabout 01; Map Update Time (ms): 10.85. ## Original sources and implementation [Original paper]() [PDF]()