In context
In 2023, global localization for mobile robots in large indoor environments remained a challenge, especially when environmental features are sparse. Most existing methods relied on extracting feature points and descriptors from laser scans or grid maps, which often failed in such settings. This paper, published in the journal Robot, addressed this gap by improving the correlative scan matching (CSM) algorithm, a technique known for its accuracy but limited by computational cost.
What was reported
Researchers from China Jiliang University proposed an improved CSM algorithm for global localization in large indoor scenes. The method calculates the contribution of each point to pose solving, using it to refine point cloud downsampling and angular step-size selection. This approach filters out less informative points, retaining those that significantly aid in determining the robot's position and orientation.
In comparative experiments conducted in both simulated and real environments, the improved algorithm demonstrated a localization success rate increase of approximately 1.5% and reduced processing time by about 1.1 seconds compared to the original CSM algorithm. The enhancements make the algorithm more suitable for large indoor environments where traditional feature-based methods struggle.
The study also addressed practical issues such as the presence of ground and ceiling points that become noise after projecting 3D point clouds to 2D, and the need for uniform point distribution to improve matching efficiency.
Why it mattered
This work offers a practical solution for mobile robots operating in large indoor spaces, such as those used for inspection tasks. By improving the speed and reliability of global localization, it supports the deployment of autonomous robots in complex industrial and commercial environments, reducing downtime and enhancing operational efficiency.
"Compared with the original CSM algorithm, the localization success rate of the improved algorithm is increased by 1.5%, and the time consumption is reduced by 1.1 s."
Source: 《机器人》期刊 (robot.sia.cn) · Published 2023-09-16 · “基于改进相关性扫描匹配的室内大场景下移动机器人的全局定位方法”