A 3D map merge method based on maximum clique algorithm
Zhiqiang Zhang, Yanduo Zhang, Tao Lü, Xun Li, Aibo Xu, Jijun Xu · 2024
Multiple robots are more effective in mapping unknown environments than a single robot. Time reduction resulting from parallelization is crucial for surveying complex areas. Multi-robot mapping, there are commonly two solutions. In the first solution, robots exchange raw data from sensors. The second method assumes that each robot independently creates a local map and exchanges and integrates it with other robots. In this paper, we propose a 3D map merge framework and a fine registration method using purely geometric maximal cliques that enhances efficiency through the utilization of overlapping regions in feature-based alignment processes. This algorithm does not require any initial guessing about the conversion between local maps. However, for successful integration, the map needs to have a common area. We demonstrate the effectiveness of the implemented methods in various environments.