论文标题
欧姆：基于GPU的占用地图生成
OHM: GPU Based Occupancy Map Generation
论文作者
论文摘要
占用网格图（OGM）是大多数自动机器人导航系统的基础。但是，基于CPU的实施努力能够跟上现代3D激光雷达传感器的数据速率，并为维持更丰富的体素表示的现代扩展提供了很小的能力。本文介绍了我们的开源，基于GPU的OGM框架。我们展示了如何将算法映射到GPU资源上，从而解决了争夺成功实施的困难。该实施支持许多现代的OGM算法，包括NDT-OM，NDT-TM，Decay-fate和TSDF。基于跟踪和四倍的UGV平台和无人机以及来自户外和地下环境的数据集进行了彻底的性能评估。结果表明，离线和在线处理中的绩效改进都出色。最后，我们描述了欧姆如何成为我们参加DARPA Subterranean Challenge的UGV导航解决方案的关键推动力，该挑战在最后的比赛中排名第二。
Occupancy grid maps (OGMs) are fundamental to most systems for autonomous robotic navigation. However, CPU-based implementations struggle to keep up with data rates from modern 3D lidar sensors, and provide little capacity for modern extensions which maintain richer voxel representations. This paper presents OHM, our open source, GPU-based OGM framework. We show how the algorithms can be mapped to GPU resources, resolving difficulties with contention to obtain a successful implementation. The implementation supports many modern OGM algorithms including NDT-OM, NDT-TM, decay-rate and TSDF. A thorough performance evaluation is presented based on tracked and quadruped UGV platforms and UAVs, and data sets from both outdoor and subterranean environments. The results demonstrate excellent performance improvements both offline, and for online processing in embedded platforms. Finally, we describe how OHM was a key enabler for the UGV navigation solution for our entry in the DARPA Subterranean Challenge, which placed second at the Final Event.
