Abstract
In this article, cooperative simultaneous localization and mapping algorithm based on distributed particle filter is proposed for multi-robot cooperative simultaneous localization and mapping system. First, a multi-robot cooperative simultaneous localization and mapping system model is established based on Rao-Blackwellised particle filter and simultaneous localization and mapping (FastSLAM 2.0) algorithm, and an median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm combined with the M-posterior distributed estimation algorithm is proposed. Then, according to the accuracy advantage of the early landmarks comparing to the later landmarks in the simultaneous localization and mapping task, an improved time-median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm based on time difference optimization is proposed, which optimizes the weights of the local estimation and improves the accuracy of the global estimation. The simulation results show that the algorithm is practical and effective.
Highlights
Multi-robot system outperforms single-robot systems in the efficiency of performing tasks, fault tolerance, robustness, reconfigurability, and hardware cost.[1]
The difference is the complexity of the system and the data types of the local estimation, so the related distributed particle filter algorithm cannot be directly transplanted to the multi-robot cooperative simultaneous localization and mapping (CSLAM) system
This article proposes a multi-robot MP-CSLAM algorithm based on the M-posterior estimation method
Summary
Multi-robot system outperforms single-robot systems in the efficiency of performing tasks, fault tolerance, robustness, reconfigurability, and hardware cost.[1]. The filtering process of each node is regarded as a sub-filter.[16] The output information of the sub-filter is the input of the fusion filter, and the fusion filter completes the estimation from local information to global information Whether it is a WSN or a single-robot SLAM system, they are quite different from the multi-robot CSLAM system.[13,17,18,19,20,21] The difference is the complexity of the system and the data types of the local estimation, so the related distributed particle filter algorithm cannot be directly transplanted to the multi-robot CSLAM system. The first problem in this article is to solve the feasibility of the algorithm, improve the traditional fusion filtering process, and propose a distributed particle filtering method suitable for multi-robot CSLAM system. The situation introduced in this article has two robots, so the robot node dimension is NV 1⁄4 2, and i m j n
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