提出高效映射框架，让机器人在复杂环境更安全快速规划路径。
Parallel OctoMapping: A Scalable Framework for Enhanced Path Planning in Autonomous Navigation
- 基于OctoMap改进，多线程并行优化自由空间表示
- 固定分辨率下提升路径成功率，路径长度减少18%以上
- 兼容现有规划器，适合部署于真实机器人导航系统
地图构建是机器人与自主系统中的关键环节，为路径规划提供空间基础。高效的地图构建使规划算法能在复杂环境中实时生成可靠路径，确保安全并动态适应。传统固定分辨率地图常导致障碍物表示过于保守，在拥挤场景中引发次优路径或规划失败。为此，本文提出并行八叉树映射（Parallel OctoMapping, POMP），一种基于OctoMap的高效映射方法，可最大化可用自由空间，支持多线程计算。据我们所知，POMP是首个在固定占用网格分辨率下，精炼自由空间表示同时保持地图保真度与现有基于搜索的规划器兼容性的方法。该方法可无缝集成至现有规划流水线，在拥挤环境中显著提升路径规划成功率、缩短路径长度，并大幅提高计算效率。
原文摘要 · Abstract (English)
Mapping is essential in robotics and autonomous systems because it provides the spatial foundation for path planning. Efficient mapping enables planning algorithms to generate reliable paths while ensuring safety and adapting in real time to complex environments. Fixed-resolution mapping methods often produce overly conservative obstacle representations that lead to suboptimal paths or planning failures in cluttered scenes. To address this issue, we introduce Parallel OctoMapping (POMP), an efficient OctoMap-based mapping technique that maximizes available free space and supports multi-threaded computation. To the best of our knowledge, POMP is the first method that, at a fixed occupancy-grid resolution, refines the representation of free space while preserving map fidelity and compatibility with existing search-based planners. It can therefore be integrated into existing planning pipelines, yielding higher pathfinding success rates and shorter path lengths, especially in cluttered environments, while substantially improving computational efficiency.
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