D-BDM: A Direct and Efficient Boundary-Based Occupancy Grid Mapping Framework for LiDARs

2026-04-14

AI Summary

Traditional 3D occupancy grid mapping faces significant challenges in unknown environments, including high memory consumption and substantial update latency, which hinder its applicability for autonomous robots requiring efficient and scalable mapping. This work proposes a boundary-based occupancy mapping framework that innovatively integrates truncated ray casting with a direct boundary update mechanism. By eliminating the need for auxiliary local voxel grids, the method avoids storing voxels across the entire space and bypasses exhaustive ray traversal. Experimental results on public datasets demonstrate that the proposed approach substantially outperforms existing baseline and boundary-aware methods, achieving comparable mapping accuracy while significantly reducing both memory usage and update time.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• occupancy grid mapping
• LiDAR
• memory consumption
• ray casting
• 3D mapping

Innovation

• boundary-based mapping
• truncated ray casting
• direct boundary update
• occupancy grid mapping
• LiDAR