基于势均衡多伯努利滤波的SLAM方法

SLAM Method Based on Cardinality Balanced Multi-Bernoulli Filter

针对传统的EKF-SLAM、FastSLAM等方法存在着复杂度高、需要进行数据关联、计算量大不足等问题,提出了一种基于势均衡多伯努利滤波的SLAM(Cardinality Balanced Multi-Bernoulli-SLAM,CBMBer-SLAM)算法,该方法是一种基于随机有限集理论的滤波方法,将势均衡多伯努利滤波方法运用到地图特征估计中,克服了复杂的数据关联和地图特征点数目估计过多的问题,从而提高地图估计的整体精度,是一种用来解决水下SLAM问题比较好的新方法。通过仿真实验,将所提算法与RB-PHD-SLAM算法进行比较,仿真结果表明该算法可以有效提高地图特征估计精度。

Aiming at the shortcomings of traditional EKF-SLAM and FastSLAM methods,such as high complexity,data association and large amount of computation,a SLAM algorithm based on cardinality balanced multi-Bernoulli filter is proposed in this paper.This method applies the cardinality balanced multi-Bernoulli filtering to map feature estimation,which overcomes the problem of complex data association and excessive estimation of map feature points.Therefore,the overall accuracy of the map estimation is improved,and it is a new method for solving the underwater SLAM problem.Through simulation experiments,the proposed algorithm is compared with PHD-SLAM algorithm.The results show that the proposed algorithm can effectively improve the accuracy of map feature estimation.