Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Algorithms in C
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
Utilizing reflection properties of surfaces to improve mobile robot localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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The problem of mobile robot self-localization is considered as solved since Thrun's et. al [1] pioneering work using monte-carlo filters for robot Localization (MCL). However, MCL is robust and precise under constraints like completely known environments and the sensor data must contain enough "true data" as contained in the map. In fact these conditions cannot always be guaranteed, which may results in a poor accuracy of the localization. In this paper we present a area-based observation model that is applied to MCL self-localization. The model is based on the idea of tracking the ground area inside the "free space" (not occupied cells) of a known map. Experimental data shows that the proposed model improves the robustness and accuracy of laser and stereo vision sensors under certain conditions like incomplete map, limited FOV and limited range of sensing. We also present an efficient approximation of our sensor model based on integral images.