Vector-AMCL: Vector based adaptive monte carlo localization for indoor maps

Abstract

For navigation of mobile robots in real-world scenarios, accurate and robust localization is a fundamental requirement. In this work we present an efficient localization approach based on adaptive Monte Carlo Localization (AMCL) for large-scale indoor navigation, using vector-based CAD floor plans. The approach is able to use the line segment data of these plans directly. In order to minimize the computational effort, a visibility lookup table is generated, reducing the amount of line segments to process for pose estimation. In addition, we show that the proposed approach performs well in cluttered as well as uncluttered environments. It is compared with grid map-based AMCL and is able to improve its results in terms of memory usage and accuracy.