Grid Map Fusion of Different Resolutions Based on Density Clustering
Honghui Tao, Bo Ji, Yuheng Gao · 2024
Map fusion is a key challenge in multi-robot SLAM systems, with feature-based methods often leading to mismatches when handling maps of different resolutions. This paper introduces a density clustering-based map fusion method that reduces mismatches by clustering features before matching them. Experiments on public datasets demonstrate that the proposed method effectively merges grid maps of varying resolutions, offering improved efficiency and accuracy compared to traditional feature-based methods.