Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Experimental Results got with the Omnidirectional Vision Sensor: Syclop
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Panoramic Mosaicing with a 180° Field of View Lens
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
People detection through quantified fuzzy temporal rules
Pattern Recognition
IEEE Transactions on Robotics
Omnidirectional vision scan matching for robot localization in dynamic environments
IEEE Transactions on Robotics
Robotics and Autonomous Systems
Kullback-Leibler divergence-based global localization for mobile robots
Robotics and Autonomous Systems
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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 problems with occlusions, real-time operation, and environment modifications. In this article, an omnivision-based MCL algorithm that solves these drawbacks is presented. The algorithm works with 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 process makes 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 environments.