基于EKF算法的小地块SLAM技术研究与分析

Research and Analysis of Small Plot SLAM Technology Based on EKF Algorithm

针对小地块环境GPS信号弱、精度不足,难以形成精确地图及定位以实现农用智能机械自主作业的问题,课题组提出一种基于扩展卡尔曼滤波算法的同步定位与建图技术。首先,建立了机器人的运动方程和观测方程,并基于扩展卡尔曼滤波和同步定位与建图技术原理,提出了机器人作业过程的信息预测和更新理论基础。其次,通过数值仿真实现农用小车在未知小地块果园中的定位与建图,并在转弯处设置路标点稠密对比实验,分析了算法的性能和精度。仿真实验表明,本课题算法可有效地应用于小地块果园环境,通过在转弯处增加路标点,可提高算法在小地块的位置估计精度。

In the small plot environment,the GPS signal is weak and the accuracy is insufficient,so it is difficult to form accurate maps and positioning to realize the independent operation of agricultural intelligent machinery.Aiming at this problem,a simultaneous localization and mapping technique based on extended Kalman filtering algorithm is proposed.Firstly,the robot’s motion equation and observation equation are established,and the theoretical basis for the prediction and update of the robot’s operation process are proposed based on the principle of extended Kalman filtering and simultaneous localization and mapping technology.Then,the numerical simulation is an implement to realize,the localization and mapping of the agricultural trolley in the unknown small orchard.To analyze the performance and accuracy of the algorithm,comparison experiment of dense landmarks in turning points is carried out.The simulation experiment shows that the present algorithm is effective for the environment of small plots orchards.By adding road signs at turning points,the accuracy of the position estimation in small plots can be improved.