Omnivision-based KLD-Monte Carlo Localization

Mobile robots operating in real and populated environments usually execute tasks that require accurate knowledge on their position. Monte Carlo Localization (MCL) algorithms have been successfully applied for laser range finders. However, vision-based approaches present several problemswith occlusions, realtime operation, and environment modifications. In this article, an omnivision-based MCL algorithm that solves these drawbacks is presented. The algorithmworkswith a variable number of particles through the use of the Kullback–Leibler divergence (KLD). The measurement model is based on an omnidirectional camera with a fish-eye lens. This model uses a feature-based map of the environment and the feature extraction processmakes it robust to occlusions and changes in the environment.Moreover, the algorithm is scalable and works in real-time. Results on tracking, global localization and kidnapped robot problem show the excellent performance of the localization system in a real environment. In addition, experiments under severe and continuous occlusions reflect the ability of the algorithm to localize the robot in crowded