基于因子图优化的激光SLAM

为提高基于激光雷达的同步定位和建图(simultaneous localization and mapping,SLAM)精度，提出一种基于因子图的高效率、高精度的激光雷达SLAM框架。采用一种基于滑动窗口的因子图方法，将当前帧进行帧间匹配得到相对位姿，按照一定规则选出关键帧，将关键帧与全局地图进行匹配得到绝对位姿；构建一个因子图，将得到的连续帧之间的相对位姿与关键帧的绝对位姿作为优化因子，机器人的位姿作为状态节点放入因子图中进行位姿优化，得到高频率的机器人位姿以及全局一致的环境地图。结果表明：该算法能够减小误差的累积，具有更高的定位精度。

Laser SLAM Based on Factor Graph Optimization

In order to improve the accuracy of simultaneous localization and mapping (SLAM) based on laser radar, an efficient and high-precision SLAM framework based on factor graph is proposed in this paper. A factor graph method based on sliding window is used to match the current frame to get the relative pose, and then the key frame is selected according to certain rules, and the absolute pose is obtained by matching the key frame with the global map. A factor graph is constructed, and the relative pose between consecutive frames and the absolute pose of key frames are taken as optimization factors, and the pose of the robot is taken as a state node to be put into the factor graph for pose optimization, so that the pose of the robot with high frequency and a globally consistent environment map are obtained. The results show that the algorithm can reduce the accumulation of errors and has higher positioning accuracy.