位姿自适应卡尔曼滤波SLAM信标循迹的研究

Research on Pose Adaptive Kalman Filtering of SL AM Beacon Tracking

实时定位和地图构建算法(SLAM)作为一种机器人定位并自主循迹行驶的重要方法,其位姿估计和关键点信标循迹的研究是一项核心问题。针对现有各类改进的SLAM算法在信标循迹的定位效率低、循迹精度差、总体耗时长等缺陷,提出了一种位姿自适应的卡尔曼滤波SLAM算法。针对SLAM系统的数学模型,建立了位姿自适应的基本描述方法,结合卡尔曼滤波对SLAM进行整体优化,消除了噪声干扰造成的系统偏差。通过计算机仿真实验研究,相对于Hybrid-SLAM、Fast-SLAM、EK-SLAM三种改进的SLAM算法,模拟机器人在不同随机采样点的位移偏差趋势角度更低,同时信标点定位总精度分别提升了0.219 m、0.236 m、0.437 m,平均角度偏差分别提升了2.14°、1.76°、1.18°,循迹时间分别提升了8.543 s、11.416 s、11.878 s。改进方法在关键点信标定位、路径规划和自动驾驶等方面的具有较好的应用价值。

Simultaneous Localization and Mapping algorithm(SLAM) is an important method for robot localization and autonomous tracking,and the study of pose estimation and keypoint beacon tracking is a core issue. Aiming at the shortcomings of the current various improved SLAM algorithms in the beacon tracking,such as low positioning efficiency,poor tracking accuracy,and overall time consuming,a pose adaptive Kalman filter SLAM algorithm is proposed. Aiming at the mathematical model of the SLAM system,a basic description method of posture adaptation is established,combine with Kalman filter to optimize the SLAM system,and to eliminate the system deviation caused by noise interference. Through computer simulation experiments,compared to the three improved SLAM algorithms of Hybrid-SLAM,Fast-SLAM,and EK-SLAM,the simulation robot has lower displacement deviation trend angles at different random sampling points,and the overall accuracy of beacon point positioning has been improved. At the same time,the total accuracy of beacon point positioning has been improved by 0. 219 m,0. 236 m,and 0. 437 m,the average angle deviation has been increased by 2. 14°,1. 76°,and1. 18°,and the tracking time is increased by 8. 543 s,11. 416 s,and 11. 878 s,respectively. It has good application value in key point beacon positioning,path planning and automatic driving.