Fast ICP-SLAM for a bi-steerable mobile robot in large environments

Abstract

This paper describes the implementation of a local ICP-SLAM (Iterative Closest Point - Simultaneous Localization and Mapping) to improve the method presented in [1] to become faster. The ICP algorithm is known as a method that requires more computation time when the environment grows leading to poor results for both localization and mapping. Therefore, the ICP-SLAM is not recommended to use in real time for large environments. To overcome this problem, a local ICP-SLAM is introduced which is based on the partition of the environment on smaller parts. This method is implemented and tested on the car-like mobile robot “Robucar”. It allows the optimization of the computation time and localization accuracy. The experimental results show the effectiveness of the proposed local ICP-SLAM compared to the method in [1].