VinySLAM: An indoor SLAM method for low-cost platforms based on the Transferable Belief Model

A truly autonomous mobile robot have to solve the SLAM problem (i.e. simultaneous map building and pose estimation) in order to navigate in an unknown environment. Unfortunately, a universal solution for the problem hasn't been proposed yet. The tinySLAM algorithm that has a compact and clear code was designed to solve SLAM in an indoor environment using a noisy laser scanner. This paper introduces the vinySLAM method that enhances tinySLAM with the Transferable Belief Model to improve its robustness and accuracy. Proposed enhancements affect scan matching and occupancy tracking keeping simplicity and clearness of the original code. The evaluation on publicly available datasets shows significant robustness and accuracy improvements.