基于Gray-EKF算法的智能农业车辆同时定位与地图创建

为了提高智能农业车辆在未知环境中同时定位与地图创建精度,将灰色预测理论和扩展卡尔曼滤波融合,提出了基于灰色扩展卡尔曼滤波的同时定位与地图创建算法。算法在传统的扩展卡尔曼滤波基础上,通过改进的滑窗灰色预测理论建立传感器的GM(1,1)观测预测模型,进而完成新息的计算。为了提高观测精度和抗干扰能力,系统使用了三目摄像机作为观测传感器,并提出了一种简易的权值标定算法。试验表明:精度权值标定后的三目摄像机具有较高的测量精度,16组测量数据中有12组的测量误差小于1%,并能减小由于干扰造成的误差。在30个人工路标的停车场环境中,车辆对路标x和y方向的观测误差均值为0.074和0.073m,自身定位误差为0.140m,误差均方差为0.048。在60个人工路标的停车场环境中,车辆对路标x和y方向的观测误差均值为0.061和0.068m,自身定位误差为0.109m,误差均方差为0.038。在60个人工路标的旱地环境中,车辆对路标x和y方向的观测误差均值为0.079和0.077m,自身定位误差为0.122m,误差均方差为0.049。研究认为,与传统的EKF SLAM算法相比,Gray-EKF SLAM算法具有更高的精度。

Simultaneous localization and mapping based on Gray EKF for intelligent agricultural vehicle

A Simultaneous Localization and Mapping (SLAM) algorithm based on Gray-EKF was designed by using gray prediction theory and EKF in order to improve the accuracy of SLAM for intelligent agricultural vehicle. A GM(1,1) prediction model of observation based on improved sliding window gray prediction was set up on the foundation of traditional EKF, and then innovations were made out. With the purpose of raising accuracy and capacity of resisting disturbance, a trinocular stereo vision camera was used as the observation sensor and an accuracy calibrating algorithm was brought out simultaneously. Experiments showed that the calibrated trinocular camera was well precision and anti-interference. The errors of 12 measuring results in 16 were less than 1%. The average observation errors were 0.074 and 0.073 m in x and y axis, and position error was 0.140 m with RMSE 0.048 in the park environment of 30 landmarks. The average observation errors were 0.061 and 0.068m in x and y axis, and position error was 0.109 m with RMSE 0.038 in the park environment of 60 landmarks. The average observation errors were 0.079 and 0.077 m in x and y axis, and position error was 0.122 m with RMSE 0.049 in the dry land environment of 60 landmarks. Compared with traditional EKF SLAM algorithm, Gray-EKF SLAM algorithm is more precise.