摘要
为了解决机器人同时定位、地图构建和目标跟踪问题,提出了一种基于交互多模滤波(interacting multiple model filter,IMM)的方法.该方法将机器人状态、目标状态和环境特征状态作为整体来构成系统状态向量并利用全关联扩展式卡尔曼滤波算法对系统状态进行估计,由此随着迭代估计的进行,系统各对象状态之间将产生足够的相关性,这种相关性能够正确反映各对象状态估计间的依赖关系,因此提高了目标跟踪的准确性.该方法进一步和传统的IMM滤波算法相结合,从而解决了目标运动模式未知性问题,IMM方法的采用使系统在完成目标追踪的同时还能对其运动模态进行估计,进而提高了该算法对于机动目标的跟踪能力.仿真实验验证了该方法对机器人和目标的运动轨迹以及目标运动模态进行估计的准确性和有效性.
A novel method was developed for synchronous localization and mapping （SLAM） and object tracking （OT） to provide simultaneous estimation of a robot＇s and any object＇s trajectories in an unknown environment. The system was based on interacting multiple model （IMM） filtering. In this approach, the states of robots, objects and landmarks were used to form an integrated system state. A full covariance extended Kalman filter （EKF） was then employed to estimate system state. As the iterative estimation progressed, sufficient correlations between the different objects in the system could be establish to reflect the interdependent relationships of estimations between different system objects. In this way the precision of object state estimation was improved. Moreover, when combined with a traditional IMM filter algorithm, this method solved the uncertainty problem for modes of object motion. With the application of IMM, the method helped robots to track objects and estimate their modes of motion, improving the accuracy of object localization. Simulation results validated the effectiveness of the proposed method in the estimation of the trajectories of robots and objects and the modes of motion of tracked targets.
出处
《智能系统学报》
2010年第2期127-138,共12页
CAAI Transactions on Intelligent Systems
关键词
IMM滤波
EKF滤波
同时定位
地图构建
目标跟踪
移动机器人
interacting multiple model filter
extended Kalman filter
simultaneous localization and mapping
object tracking
mobile robot
