ROG-Map: 大規模・高解像度LiDAR動作計画のための効率的なロボット中心占有格子地図
ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning
ロボットに追従する局所占有格子地図と新しい漸進的障害物膨張手法を提案し、大規模環境でのLiDAR動作計画を効率化した。
著者: Yunfan Ren, Yixi Cai, Fangcheng Zhu, Siqi Liang, Fu Zhang
分類: cs.RO
原文アブストラクト
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.
