Tight coupling mapping method fusing ESKF, g2o and point cloud matching

Abstract

The invention discloses a tight coupling mapping method integrating ESKF, g2o and point cloud matching. The method comprises the steps: acquiring three-dimensional point cloud data and output data ofan inertia measurement device; determining a matching initial value according to the output data of the inertial measurement device and a state error Kalman filtering equation; matching the three-dimensional point cloud data based on the matching initial value to obtain a pose relationship; optimizing the pose relationship based on a graph optimization general framework to obtain a frame pose; andbased on the frame pose, splicing the three-dimensional point cloud data to obtain a three-dimensional point cloud map. As the state error Kalman filtering equation is used for providing the initialvalue of point cloud matching for matching, and a graph optimization universal framework is fused for optimization, the speed is high, the precision is high, and the precision and the speed of point cloud matching can be effectively improved. 本发明公开了一种融合ESKF，g2o和点云匹配的紧耦合建图方法，包括步骤：获取三维点云数据和惯性测量器件的输出数据；根据所述惯性测量器件的输出数据和状态误差卡尔曼滤波方程，确定匹配初始值；基于所述匹配初始值，对所述三维点云数据进行匹配，得到位姿关系；基于图优化通用框架，对所述位姿关系进行优化，得到帧位姿；基于所述帧位姿，对所述三维点云数据进行拼接，得到三维点云地图。由于使用状态误差卡尔曼滤波方程提供点云匹配的初始值进行匹配，并融合图优化通用框架进行优化，速度快，精度高，能有效提高点云匹配的精度和速度。