基于位姿图优化的势均衡多伯努利滤波器SLAM方法

Cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization

在复杂室内环境下,传统基于随机有限集理论的SLAM方法存在机器人位姿精度低、计算量大的问题。针对此问题,提出一种基于位姿图优化的势均衡多伯努利滤波器SLAM方法。首先,该方法通过势均衡多伯努利滤波器获得地图特征估计,避免了数据关联。其次,提出了自适应信息控制法,丰富先验信息。然后,通过自适应信息控制法将位姿图优化理论与势均衡多伯努利滤波器SLAM结合,优化机器人的位姿估计。最后,进行实验对比分析,结果表明所提方法比RB-PHD-SLAM方法有更好的SLAM精度及实时性。

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.