Real-Time Robot Localization In Indoor Environments Using Structural Information

Abstract

This paper presents a novel approach for real-time mobile robot localization in structured indoor environments. The proposed method takes advantage of the available structural information by implementing a Monte Carlo Localization strategy over a map of line segments rather than a grid-based map, thus allowing for speed improvements. Another novel aspect is in the likelihood function, which is based on a Modified Hausdorff Distance between the expected line segments the robot should sense and the line segments extracted from actual measurements using a range finder. Additionally, the number of particles of the Monte Carlo Localization method is automatically adjusted to allow for a large number of particles in the global localization phase, in which the position of the robot is unknown, while the number of particles is reduced during the state tracking phase, in which uncertainty of the robot’s position is restricted to a smaller area. The proposed approach has been implemented and tested in a real office environment, achieving true real-time performance. Results show the approach’s fast convergence from global localization to state tracking, as well as its robustness in position tracking. Experimental tests and comparisons with other state of the art methods validate the method’s efficiency and robustness.