基于扩展式卡尔曼滤波的移动机器人未知环境下动态目标跟踪

Extended Kalman Filter Based Moving Object Tracking by Mobile Robot in Unknown Environment

为了解决机器人在未知环境下的动态目标追踪问题,提出了一种基于扩展式卡尔曼滤波的估计算法.该算法将机器人、环境特征以及目标状态作为整体来构成系统状态,因此在迭代过程中系统各对象状态能够逐步建立起足够的关联性,从而提高了目标状态估计的准确性.进一步将该算法和基于占用栅格地图的动态物体检测方法相结合以获取目标和环境观测值,使算法最终能够应用于实际环境.另外,算法设计的数据关联环节能够有效处理目标伪观测值对系统状态估计的干扰.仿真实验和实体机器人实验结果验证了该算法的准确性和有效性.

In order to solve the problem of moving object tracking by robot in unknown environment,an estimation algorithm based on extended Kalman filter（EKF） is proposed.The states of robot,environment feature and object are used to form system state as a whole in the algorithm,such that sufficient relation is established gradually among states of different objects in iteration process,which improves accuracy of object state estimation.Moreover,a method of moving object detection based on occupancy grid map is combined with our algorithm to obtain the measurements of moving object and environment landmarks,so that the final algorithm can be used in actual environment.Furthermore,the step of data association proposed in algorithm can deal with the system state estimation disturbance caused by false object observations.Simulation experiment and real robot experiment results prove the effectiveness and accuracy of the presented approach.