RIPNEON: Memory-Lite and Computation-Efficient Occupancy Mapping Via Block Read-Write and Key Grids Expansion
Qianli Dong, Xuebo Zhang, Shiyong Zhang, Haobo Xi, Ziyu Wang, Zhe Ma, Zhiyong Zhang
AI summary
Problem
Existing LiDAR occupancy mapping methods consume excessive memory and computation due to full-map maintenance and repetitive ray-casting queries, hindering real-time robotic exploration in large environments.
Approach
The method stores map data in dynamically managed grid blocks and replaces ray-casting with a key grids expansion algorithm that uses breadth-first search to efficiently update free and frontier grids.
Key results
- 23.1%–94.1% lower memory usage in large-scale mapping
- 5.7%–78.3% higher map update efficiency
- Simultaneous frontier grid detection without extra computation
- Validated via real-world UAV exploration experiments
Why it matters
Enables scalable, real-time LiDAR exploration for resource-constrained robots operating in large-scale environments.
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 and computation-efficient occupancy mapping algorithm for LiDAR-based robotic exploration plan- ning. To accelerate the query operation and reduce the memory usage, we adopt the grid-block 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.