This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments comparing Ceres and g2o within Cartographer, Ceres outperformed g2o in terms of speed, convergence efficiency, and overall map clarity. Ceres required fewer iterations and less time to converge, producing more accurate and well-defined maps, especially in real-world mapping scenarios with the AgileX LIMO robot. However, g2o excelled in localized obstacle detection, highlighting its value in specific situations.

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.