Recent advances in LiDAR technology have opened up new possibilities for robotic navigation. Given the widespread use of occupancy grid maps (OGMs) in robotic motion planning, this paper aims to address the challenges of integrating LiDAR with OGMs. To this end, we propose ROG-Map, a uniform grid-based OGM that maintains a local map moving along with the robot to enable efficient map operation and reduce memory costs for large-scene autonomous flight. Moreover, we present a novel incremental obstacle inflation method that significantly reduces the computational cost of inflation. The proposed method outperforms state-of-the-art (SOTA) methods on various public datasets. To demonstrate the effectiveness and efficiency of ROG-Map, we integrate it into a complete quadrotor system and perform autonomous flights against both small obstacles and large-scale scenes. During real-world flight tests with a 0.05 m resolution local map and 30mx30mx12m local map size, ROG-Map takes only 29.8% of frame time on average to update the map at a frame rate of 50 Hz (\ie, 5.96 ms in 20 ms), including 0.33% (i.e., 0.66 ms) to perform obstacle inflation, demonstrating outstanding real-world performance. We release ROG-Map as an open-source ROS package to promote the development of LiDAR-based motion planning.

翻译：近年来激光雷达技术的进步为机器人导航开辟了新的可能性。鉴于占据栅格地图（OGMs）在机器人运动规划中的广泛应用，本文旨在解决激光雷达与OGMs集成面临的挑战。为此，我们提出ROG-Map——一种基于均匀网格的OGM，通过维护随机器人移动的局部地图，实现高效地图操作并降低大场景自主飞行的内存成本。此外，我们提出一种新颖的增量式障碍物膨胀方法，显著降低了膨胀的计算成本。该方法在多个公开数据集上优于现有最先进（SOTA）方法。为验证ROG-Map的有效性与高效性，我们将其集成到完整四旋翼系统中，并在小型障碍物与大尺度场景下开展自主飞行实验。在实测飞行中，采用0.05米分辨率局部地图和30m×30m×12m局部地图尺寸，ROG-Map在50Hz帧率下平均仅占用29.8%的帧时间进行地图更新（即20ms内耗时5.96ms），其中包含0.33%（即0.66ms）用于障碍物膨胀，展现出卓越的实际性能。我们已将ROG-Map作为开源ROS软件包发布，以推动基于激光雷达的运动规划发展。