计算机工程与科学2012，Vol.34Issue(7)：140-145,6.DOI:10.3969/j.issn.1007-130X.2012.07.026
基于精确稀疏扩展信息滤波的粒子滤波SLAM算法研究
Research on the Particle Filter SLAM Algorithm Based on Exactly Sparse Extended Information Filter
摘要
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.关键词
同时定位与地图创建/精确稀疏扩展信息滤波/粒子滤波/Gibbs采样Key words
simultaneous localization and map building (SALM)/ exactly sparse extended information filter/particle filter/Gibbs sample分类
信息技术与安全科学引用本文复制引用
朱代先,王晓华..基于精确稀疏扩展信息滤波的粒子滤波SLAM算法研究[J].计算机工程与科学,2012,34(7):140-145,6.基金项目
国家自然科学基金资助项目(61101146) （61101146）
陕西省教育厅自然科学专项(2010JK666) （2010JK666）
