LCR-SMM: Large Convergence Region Semantic Map Matching Through Expectation Maximization

Abstract

The matching and fusion 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, which typically fails in cases with large initial errors. What’s more, current semantic matching algorithms have high computation complexity in optimizing the transformation matrix. To address the challenge of large initial errors and low matching efficiency, this article proposes a novel large convergence region semantic map matching algorithm. 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.