HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Line Extraction in 2D Range Images for Mobile Robotics
Journal of Intelligent and Robotic Systems
A global localization approach based on Line-segment Relation Matching technique
Robotics and Autonomous Systems
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This paper describes an efficient localization algorithm based on a vector-matching technique for mobile robots with laser range finders. As a reference the method uses a map with line-segment vectors, which can be built from a CAD layout of the indoor environment. The contribution of this work lies in the overall localization process. First, the proposed sequential segmentation method enables efficient vector extraction from scanned data. Second, a reliable and efficient vector-matching technique is proposed. Finally, a cost function suitable for vector-matching is proposed for nonlinear pose estimation with solutions for both nonsingular and singular cases. Simulation results show that the proposed localization method works reliably even in a noisy environment. The algorithm was implemented for our wheelchair-based mobile robot and evaluated in a 135 m long corridor environment.