From omnidirectional images to hierarchical localization
Robotics and Autonomous Systems
Incremental pose estimation for mobile robots within curvilinear environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Localization system for mobile robot using wireless communication with IR landmark
Proceedings of the 1st international conference on Robot communication and coordination
A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor
IEICE - Transactions on Information and Systems
Active single landmark based global localization of autonomous mobile robots
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Scale Invariant Feature Transform on the Sphere: Theory and Applications
International Journal of Computer Vision
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Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRQ global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data.