Multi-robot cardinality-balanced multi-Bernoulli filter simultaneous localization and mapping method

Abstract

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 (MR-CBMber-SLAM) method 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. This method aims to overcome the problem that the multi-Bernoulli filter will overestimate the aspect of SLAM map feature estimation, and combines the strategy of balancing cardinality with a multi-Bernoulli filter. What is more, in order to further improve the accuracy and operating efficiency of SLAM, a multi-robot strategy and a multi-robot Gaussian information-fusion 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 a complex indoor environment.