Scan Matching for Graph SLAM in Indoor Dynamic Scenarios

SLAM (Simultaneous Localization And Mapping) plays an essential and important role for mobile robotic autonomous navigation. SLAM in dynamic environments with moving objects is a challenging problem. We focus on scan matching for Graph-SLAM in indoor dynamic scenarios. Scan matching algorithm is proposed and implemented, which consists of the following phases: first, conditioned Hough Transform based segmentation is performed to extract and group line features; second, occupancy-analysis based moving objects detection is done to detect and discard the segments corresponding to the moving objects; third, linear regression based line feature matching is executed to estimate the roto-translation parameters. Simulations to estimate roto-translation and the entire trajectory of the robot effectively verified the robustness of this algorithm in a dynamic scenario. The proposed algorithm is based on the line features of the indoor environment. It is robust to disturbances from moving objects in the dynamic scenario, and is especially suitable for the case when large rotational displacement is present.