A new PHD-SLAM method based on memory attenuation filter

Abstract

Aiming at the problem that the low signal-to-noise ratio in the complex indoor environment will lead to the complex data association and low accuracy of the simultaneous localization and mapping (SLAM) method, a PHD-SLAM method based on the memory attenuation (MA) filter and mixed newborn maps information (MBMA-PHD-SLAM) is proposed in this paper. First of all, this method based on a probability hypothesis density (PHD) filter. Therefore, this method avoids data association and solves the problem of high computational complexity. Besides, the general PHD-SLAM method tends to cause the filter to diverge when the indoor signal-to-noise ratio is low. Therefore, MA filter is combined into PHD-SLAM. This method can reduce the influence of old data on SLAM, which improves the filtering divergence problem and improves the accuracy of SLAM. What’s more, since conventional SLAM algorithms usually have a problem of lack of prior information and in order to further improve the accuracy of SLAM on the basis of the above method, a method of mixed newborn maps information is proposed to solve this problem. Finally, the experiment mainly compares the method in this paper with the classic Rao-Blackwellised (RB) implementation of the PHD-SLAM (RB-PHD-SLAM). The results show that this method outperforms the PHD-SLAM method in terms of estimating map features and localization accuracy. The method in this paper effectively improves the ability of the mobile robot to explore unknown indoor environments.