Randomized Algorithms for Minimum Distance Localization

Abstract

We address the problem of minimum distance localization in environments that may contain self-similarities. 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 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. We present two randomized approximation algorithms that solve minimum distance localization. The performance of our algorithms is evaluated empirically.

Research supported in part by NSERC and FCAR.