A new resampling algorithm for particle filters and its application in global localization within symmetric environments

Abstract

Mobile robots are undergoing tremendous development, which makes them employed in many fields. In this area, global localization in symmetric indoor environments is a commonly encountered problem. One of the commonly used algorithms to solve it, is the Adaptive Monte Carlo Localization (AMCL), which is based on the particle filter algorithm. In this paper, we developed a new algorithm for resampling used within the Adaptive Monte Carlo Localization (AMCL) framework that we called Effective Samples Resampling (ESR). The proposed algorithm is based on a deterministic sample selection, and it is thoroughly tested in real time. Using a considerable amount of simulations, the efficacy and robustness of the AMCL using this technique are validated and compared to certain conventional approaches. They are also tested and validated in various real-time operating conditions using the Robot Operating System (ROS). The obtained results are quite satisfying in terms of resampling quality, implementation complexity, and convergence time when compared to random resampling approaches where a sample-based probability density given by high-quality sensors might destabilize localization. The global localization is well handled when the proposed algorithm is involved, compared to standard resampling algorithms that can often be overconfident and fail in some scenarios when there is a lot of symmetry in the considered map of the environment.