Abstract
In order to improve the Simultaneous Localization and Mapping (SLAM) accuracy of mobile robots in complex indoor environments, the multi-robot cardinality balanced Multi-Bernoulli filter SLAM method (MR-CBMber-SLAM) is proposed. First of all, this method introduces a Multi-Bernoulli filter based on the random finite set (RFS) theory to solve the complex data association problem. Besides, this method aims at the problem that the Multi-Bernoulli filter will overestimate in the aspect of SLAM map features estimation, and combines the strategy of cardinality balanced with the Multi-Bernoulli filter. What’s more, in order to further improve the accuracy and operating efficiency of SLAM, a multi-robot strategy and a multi-robot Gaussian information fusion (MR-GIF) method are proposed. In the experiment, the MR-CBMber-SLAM method is compared with the multi-vehicle Probability Hypothesis Density SLAM (MV-PHD-SLAM) method. The experimental results show that the MR-CBMber-SLAM method is better than MV-PHD-SLAM method. Therefore, it effectively verifies that the MR-CBMber-SLAM method is more adaptable to the complex indoor environment.
Multi-Robot Information Fusion and Coordination Based on Agent