基于概率数据关联交互多模滤波的移动机器人未知环境下动态目标跟踪  

PDA-IMM Based Moving Object Tracking with Mobile Robots in Unknown Environments

作　　者：伍明[1] 李琳琳[1] 孙继银[1] 

机构地区：[1]中国人民解放军第二炮兵工程学院,陕西西安710025

出　　处：《机器人》2012年第6期668-679,共12页Robot

基　　金：国家863计划资助项目(2006AA04Z258)

摘　　要：为了解决未知环境条件下自主移动机器人机动目标跟踪问题,提出了一种概率数据关联交互多模滤波算法.算法基于全协方差扩展式卡尔曼滤波框架,系统状态由机器人状态、环境特征状态以及目标状态联合构成,采用交互多模滤波方法解决了机动目标运动过程中的模式不确定问题.针对实际应用中目标存在伪观测值的问题,在不同运动模式滤波器中采用概率数据关联方法加权计算不同观测值对系统状态更新的贡献.仿真实验验证了算法对机器人状态、环境状态以及机动运动目标状态的估计准确性,证明了算法对机动运动物体的跟踪能力,以及对于目标伪观测值的处理能力,实体机器人实验验证了算法的实用性.In order to solve the problem of maneuvering object tracking by autonomous mobile robots in unknown environ- ments, a filtering algorithm adopting probabilistic data association （PDA） and interacting multiple model （IMM） is proposed. The algorithm is based on the framework of full covariance extended Kalman filter. The robot state, environment landmark states and target state are used to form the system state, and the problem of model uncertainty in the process of object moving is solved by IMM filter. For the problem of false object observations in practical application, a PDA method is used to weight the contribution of different observations to system state update in different moving model filters. Simulation results show the accuracy of the algorithm in estimating robot state, environment landmark states and target state, and prove the algorithm ability of tracking maneuvering object, as well as the ability of dealing with false object observations. A real robot experiment verifies the practicability of the algorithm.

关 键 词：同时定位与地图构建 交互多模滤波 概率数据关联滤波 目标跟踪 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]