移动机器人SLAM改进算法的分析与实现

为解决移动机器人扩展卡尔曼滤波(EKF-SLAM)算法计算复杂、精确度不高及易受干扰的缺点，提出一 种基于最优平滑滤波理论的改进同步定位与地图构建(simultaneous localization and mapping，SLAM)算法。详细介绍 算法的改进过程，通过Matlab 软件对其位置轨迹跟踪误差及标准差进行仿真分析，基于机器人操作系统(robot operating system，ROS)系统的实验平台，在室内走廊进行SLAM 实验以测试改进算法的效果。结果表明，改进的 SLAM 算法精度高、抗干扰能力强，能实现移动机器人的即时定位与地图构建。基于ROS 系统的软件平台能简化开 发难度，提升移动机器人的智能化。

Analysis and Implementation of Improved SLAM Algorithm for Mobile Robot

In view of the complexity, the low precision and the interference of the EKF-SLAM algorithm of mobile robot, an improved simultaneous localization and mapping (SLAM) algorithm based on the optimal smoothing filtering theory is proposed. The improvement process of the algorithm is introduced in detail, and the position tracking error and standard deviation of the position tracking software are simulated and analyzed by MATLAB software to verify the superiority of the improved algorithm. Finally, an experimental platform based on robot operating system (ROS) system is designed and SLAM experiment is carried out in the indoor corridor to test the effect of the improved algorithm. The results show that the improved SLAM algorithm has high precision and strong anti-interference ability, and it can realize simultaneous localization and map building of mobile robot. At the same time, the software platform based on ROS system simplifies the difficulty of development and improves the intelligence of mobile robot.