Absolute Indoor Positioning-aided Laser-based Particle Filter Localization with a Refinement Stage

Abstract

Robot localization in indoor environments is crucial for achieving flexible automated navigation. In this paper, we propose a novel multi-stage localization approach for mobile robots which combines a commercial beacon-based absolute indoor positioning system with laser scan data. This configuration of sensors can be deployed in a non-intrusive way in most robotic platforms without the use of proprioceptive sensors (e.g. wheel encoders) which often introduce non-negligible maintenance and downtime costs. The data fusion is performed by a particle filter aided by a refinement stage. The proposed approach was evaluated in two different indoor scenarios with a mobile platform equipped with a mobile Marvelmind beacon and a Hokuyo UTM-30LX scanning laser rangefinder. The proposed localization framework, purposely without proprioceptive data, is compared with an AMCL approach having as inputs odometry, calculated from wheel encoders' data, and laser scan data. Preliminary results show that the proposed approach can provide an accurate localization estimate and that using the refinement stage improves localization.