一种未知环境下机器人多目标跟踪算法

摘要

In this paper, a particle filtering algorithm based on the joint integrated probabilistic data association ( JIPDA) is proposed in order to solve the problem of motile robot multi⁃object tracking in unknown environments. The Rao⁃Blackwellized particle filtering is reconstructed based on the JIPDA in the new algorithm. It allows the ro⁃bot to estimate joint states of itself, environment features and multi⁃object states simultaneously. The algorithm di⁃vides the system variables into two parts: the lineal variable representing multi⁃object and environment feature states, and the non⁃linear variable representing robot states. The system state is updated by JIPDA Kalman filtering and particle filtering. Estimation precision of robot states, environment feature states and multi⁃object states is veri⁃fied by simulation results, verifying the ability of multi⁃object tracking in unknown environments.%针对未知环境下移动机器人多目标跟踪问题，设计了一种基于联合概率数据关联的粒子滤波算法。该算法利用联合概率数据关联方法对Rao⁃Blackwellized粒子滤波算法进行改进，使机器人能够完成未知环境条件下对自身状态、环境特征状态和多目标状态的在线联合估计。算法将系统状态变量分为代表多目标、环境特征状态的线性变量和代表机器人状态的非线性变量，并利用联合概率数据关联Kalman滤波和粒子滤波对系统状态进行更新。通过仿真实验证明了该算法对机器人状态、环境特征状态以及多目标状态的估计准确性，验证了算法对未知环境下多目标的跟踪能力。