A linear-time algorithm for solving the strong hidden-line problem in a simple polygon
Pattern Recognition Letters
An optimal algorithm for intersecting line segments in the plane
Journal of the ACM (JACM)
The Robot Localization Problem
SIAM Journal on Computing
Localizing a Robot with Minimum Travel
SIAM Journal on Computing
On finding narrow passages with probabilistic roadmap planners
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Computational principles of mobile robotics
Computational principles of mobile robotics
Optimal robot localization in trees
Information and Computation
Computational Geometry in C
Efficient Robot Self-Localization in Simple Polygons
Intelligent Robots: Sensing, Modeling and Planning [Dagstuhl Workshop, September 1-6, 1996]
A near-tight approximation lower bound and algorithm for the kidnapped robot problem
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
The localization problem for mobile robots
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Localization: approximation and performance bounds to minimize travel distance
IEEE Transactions on Robotics
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
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The problem of minimum distance localization in environments that may contain self-similarities is addressed. A mobile robot is placed at an unknown location inside a 2D self-similar polygonal environment P. The robot has a map of P and can compute visibility data through sensing. However, the self-similarities in the environment mean that the same visibility data may correspond to several different locations. The goal, therefore, is to determine the robot's true initial location while minimizing the distance traveled by the robot. Two randomized approximation algorithms are presented that solve minimum distance localization. The performance of the proposed algorithms is evaluated empirically.