Scan Matching for Graph SLAM in Indoor Dynamic Scenarios

Abstract

SLAM (Simultaneous Localization And Mapping) plays an essential and important role for mobile robotic autonomous navigation. SLAM in dynamic environ- ments with moving objects is a challenging problem. We focus on scan matching for Graph-SLAM in indoor dynamic scenarios. Scan matching algorithm is pro- posed and implemented, which consists of the follow- ing phases: first, conditioned Hough Transform based segmentation is performed to extract and group line features; second, occupancy-analysis based moving ob- jects detection is done to detect and discard the seg- ments corresponding to the moving objects; third, lin- ear 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 al- gorithm 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.