Abstract:

Historical information can not be fully utilized because of the small weight particle removed in resampling and the single iteration of the particle filter algorithm, thus there is degradation of the particles, and the estimation accuracy of the filtering algorithm is low. A SLAM algorithm based on exactly sparse extended information filter is put forward, the nonzero elements in a natural sparse information matrix of the exactly sparse extended information filter not only reflect the relative variations, but also correspond to the conditional probability of posterior probability related to the robot’s state. And with the help of the Gibbs sampling, a new sample occurs from the SLAM complete posterior distribution. Then uncertain information included in the information matrix is made full advantage of to lower any degradation possibility of the samples, keep the diversity of particle, and ease particle degradation. The results show that, the particle set gained from the above can describe the real posterior distribution in detail and improve the accuracy of the calculation of our SLAM algorithm.

Key words: simultaneous localization and map building (SALM);exactly sparse extended information filter;particle filter;Gibbs sample