为提高自动驾驶扫地机器人的环境点云重建精度,提出一种基于三维激光的图优化即时定位与建图算法。首先使用扩展卡尔曼滤波融合GPS、惯性测量单元(IMU)、里程计信息得到当前位姿,然后基于3D-NDT配准得到点云变换关系,最后通过构建图优化模型来进行后端优化,将点云位姿构建为图节点,将实时激光点云数据、融合后定位信息与地面参数作为边约束,并求解出点云的优化位姿。结果显示,与其他仅利用激光数据建图的算法相比,本算法改善了点云环境建图结果,提高了建图精度。算法的正确性和高效性得以验证。

In order to improve the accuracy of point cloud reconstruction for automatic drive sweeping robots, a simultaneous localization and mapping (SALM) algorithm based on graph optimization is proposed. First, the extended Kalman filter is used to fuse the information of GPS, inertial measurement unit (IMU) and odometer to get the current position. Second, the point cloud transformation relationship is obtained based on 3D-NDT registration. Finally, by constructing point clouds as map nodes, GPS and ground parameters as edge constraints, the back-end optimization is carried out by constructing a map optimization model. The point cloud posture is constructed as a map node, and the real-time laser point cloud data, fusion location information and ground parameters are used as edge constraints, and solve the optimum position and posture of point clouds. The results show that comparing with mapping algorithms that just based on laser data, the proposed algorithm can improve the mapping results of point cloud environment and improve the mapping accuracy. The correctness and efficiency of the strategy in this paper is verified.