Abstract:

A novel simultaneous localization and mapping (SLAM) algorithm based on the random finite set (RFS) theory is proposed, it models environmental map and sensor observations with RFS, and establishes the RFS of joint target state variable. The algorithm framework is Based on Bayesian estimator, uses a probability hypothesis density filter which is realized by particle filter to estimate robot’s poses and environmental map simultaneously. The new algorithm avoids the data association and describes the multifeature-multiobserve characteristics more accurately and naturally as well as multiple sensor information. Simulations are presented to compare the performance of the new algorithm with that of the FastSLAM 2.0, the simulation results verify the superiority of the new algorithm.