RIPNEON: Memory-Lite and Computation-Efficient Occupancy Mapping Via Block Read-Write and Key Grids Expansion

Abstract

Mobile robot motion planning heavily relies on grid-based occupancy maps, while existing works require high memory usage and expensive updating overhead. In this work, we propose a memory-lite grid-block data structure and an efficient map updating algorithm for LiDAR-based online exploration-oriented planning. To accelerate a query operation and reduce the memory usage, we adopt the grid-block-based map as the basic data structure and propose to dynamically read and write blocks around the sensor. For each block, the occupied grids and frontier grids are maintained in two separate lists, serving as key grids for the map update. Instead of updating free grids by ray-racasting, we propose a key grids expansion algorithm to avoid repetitively querying grids on casted beams. The proposed algorithm not only speeds up the occupancy map update but also detects the frontier grids, which are crucial for exploration tasks, without extra computation. We compare the proposed method with state-of-the-art mapping methods on the KITTI dataset and a self-collected dataset. The proposed method outperforms other methods in terms of memory usage and map update computation. It is also deployed on a UAV for a real-world exploration test. The source code is released at: https://github.com/NKU-MobFly-Robotics/RipNeon.