A Parallel Approach for Mobile Robotic Self-Localization

Navigation is the foundation of mobile robots. In order to plan its path and successfully move in the environment a robot must know its on position in it. The problem is called self-localization and it consists of estimating the position of a robot given a map of the environment and information obtained by sensors. One of the most efficient algorithms used to address this issue is the Monte Carlo technique, which has obtained a considerable attention the scientific community due to its simplicity and precision. The complexity of this algorithm scales proportionally to the number of particles used. The larger the environment, the more particles are required for robot localization. This fact limits the use of this algorithm to medium size environments. In order to improve the efficiency of the Monte Carlo technique and allow it to be used in large environments we propose a parallel implementation. Our implementation is based on OpenMP and MPI message passing interface. Experimental results are used to show the efficiency of our approach.