ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning

TL;DR

ROG-Map is a novel, efficient, robocentric occupancy grid map designed for large-scale, high-resolution LiDAR-based motion planning, significantly reducing computational costs and enabling real-time autonomous flight in complex environments.

Contribution

The paper introduces ROG-Map, a new robocentric occupancy grid map with an incremental obstacle inflation method, improving efficiency and performance over existing methods for large-scale LiDAR-based navigation.

Findings

ROG-Map reduces map update time to 29.8% of frame time at 50 Hz.

The incremental obstacle inflation method significantly cuts computational costs.

ROG-Map outperforms state-of-the-art methods on public datasets.

Abstract

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.