2D LIDAR SLAM Based On Gauss-Newton

Aiming at the problem that the current 2D Laser SLAM method can not take the high map accuracy and low computational complexity into account, this paper uses the 2D Graph-Based Laser SLAM algorithm. In the mapping stage, the idea of constructing a global map with submaps can effectively avoid the interference of moving objects in the environment; In the phase of pose optimization, the Gauss-Newton method [5] is used to find the new observation data of each frame, which is aligned to the optimal pose of the existing map, and then the observation data is updated to the map according to the pose; In the scan matching stage, the branch and bound algorithm is used to determine the robot's pose more quickly; In the navigation phase, DWA algorithm is used for local path planning. Through the experiments and comparison with Hector SLAM [3], we get better map and navigation results.