Robust Semantic Map Matching Algorithm Based on Probabilistic Registration Model

The matching and fusing of local maps generated by multiple robots can greatly enhance the performance of relative localization and collaborative mapping. Currently, existing semantic matching methods are partly based on classical iterative closet point (ICP), which typically fail in cases with large initial error. What's more, current semantic matching algorithms have high computation complexity in optimizing the transformation matrix. To address the challenge of map matching with large initial error, this paper proposes a novel semantic map matching algorithm with large convergence region. The key novelty of this work is the designing of the initial transformation optimization algorithm and the probabilistic registration model to increase the convergence region. To reduce the initial error before the iteration process, the initial transformation matrix is optimized by estimating the credibility of the data association. At the same time, a factor reflecting the uncertainty of the initial error is calculated and introduced to the formulation of the probabilistic registration model, thereby accelerating the convergence process. The proposed algorithm is performed on public datasets and compared with existing methods, demonstrating the significant improvement in terms of matching accuracy and robustness.