Robust Multi-Robot Global Localization with Unknown Initial Pose Based on Neighbor Constraints

Multi-robot global localization (MR-GL) with un- known initial positions in a large scale environment is a chal- lenging task. The key point is the data association between dif- ferent robots’ viewpoints. It also makes traditional Appearance- based localization methods unusable. Recently, researchers have utilized the object’s semantic invariance to generate a semantic graph to address this issue. However, previous works lack robustness and are sensitive to overlap rate of maps, resulting in unpredictable performance in real-world environments. In this paper, we propose a data association algorithm based on neighbor constraints to improve the robustness of the system. We demonstrate the effectiveness of our method on three different datasets, indicating a significant improvement in robustness compared to previous works.