一种基于增广EKF的移动机器人SLAM方法

针对移动机器人同步定位与地图构建(SLAM)过程中系统测程法误差累积问题,采用测程法误差模型和车轮速度误差模型的映像关系,结合增广扩展卡尔曼滤波(AEKF)算法结构和实际机器人模型,提出了一种有效提高定位精度的SLAM方法。将机器人速度校正参数附加到卡尔曼滤波算法的向量空间中,以形成增广状态空间,同时预测和更新了SLAM初始状态空间和速度校正参数,笔者在线实时修正机器人的速度和航向角,避免积累航向角误差,从而降低了测程法误差。基于均方根误差和归一化估计方差进行了仿真实验分析,研究结果表明:与EKF-SLAM相比,所提出的方法具有更好的估计性能,使算法保持良好的一致性,大幅度提高了机器人自身定位精度和路标估计准确度。

SLAM method based on augmented EKF for mobile robot

Aiming at the problem that the accumulation of systematic odometry error in the process of simultaneous localization and mapping （ SLAM）, by adopting the relationship of the odometry error model mapped to velocity error model of each wheel and combining augmented extended Kalman filter（AEKF） algorithm structure and considering reality robot model, one SLAM method efficiently improving the precision of localization was proposed. The systematic velocity calibration parameters were appended to the state vector of EKF-SLAM algorithm becom- ing an augmented state, and then these parameters and the SLAM initial vector were predicted and updated. Through revising the robot＇s ve- locity and orientation online, the orientation error and odometry error were decreased. The root mean squared error（RMSE） and normalized estimation error squared（NEES） were tested. The results indicate that, comparing with conventional EKF-SLAM, the proposed method has better estimation performance, keeps the algorithm consistency and generates more accurate robot localization and feature map.