Testing omnidirectional vision-based Monte-Carlo localization under occlusion

One of the most challenging issue in mobile robot navigation is the localization problem in densely populated environments. In this paper, we present a new approach for vision-based localization able to solve this problem. The omnidirectional camera is used as a range finder sensitive to the distance of color transitions, whereas classical range finders, like lasers or sonars, are sensitive to the distance of the nearest obstacles. The well-known Monte-Carlo localization technique was adapted for this new type of range sensor. The system runs in real time on a low-cost pc. In this paper we present experiments, performed in a crowded RoboCup Middle-size field, proving the robustness of the approach to the occlusions of the vision sensor by moving obstacles (e.g other robots); occlusions that are very likely to occur in a real environment. Although, the system was implemented for the RoboCup environment, the system can be used in more general environments.