A quantitative study of mapping and localization algorithms on ROS based differential robot

The ability for to simultaneously map the environment and localise itself with it respect to it, is the most important element of Autonomous Vehicles. The Simultaneous localization and Mapping (SLAM) is a complex process and consumes major chunk of computational power. Many algorithms have been developed to enhance the SLAM process and is a progressing area in Robotics research. ROS is one such framework which provides multiple algorithm nodes to work with and provides a communication layer to Robots. Many of these algorithms majorly in use are HectorSLAM, Gmapping and KartoSLAM. This paper provides with a quantitative analysis of these algorithms and their performance on various parameters on a differential robot equipped with 2D Laser scanner. We Study the optimum parameters of each of these algorithms and then compare the performance of these algorithms against one another. Since computational requirement of these algorithms is expensive, we also study the variation in performance using a Nvidia Jetson TK1 Embedded board and a Personal Laptop with dedicated GPU.