Abstract:

In the complex indoor environment, the traditional SLAM method based on random finite set theory has the problems of low robot pose accuracy and large amount of calculation.To solve these problems, a cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization was proposed.First of all, the cardinalized balanced multi-Bernoulli filter was used to estimate the map features, which avoided data association.What is more, an adaptive information control method was proposed to enrich the prior information.Then, the pose graph optimization theory was combined with cardinalized balanced multi-Bernoulli filter SLAM through adaptive information control method to optimize the pose estimation of the robot.Finally, through experimental comparative analysis, the results show that this method have better SLAM accuracy and real-time performance than the RB-PHD-SLAM method.

Key words: SLAM, random finite set theory, cardinalized balanced multi-Bernoulli filter, posegraph optimization