Robust mobile robot localisation from sparse and noisy proximity readings using Hough transform and probability grids

Citation
A. Grossmann et R. Poli, Robust mobile robot localisation from sparse and noisy proximity readings using Hough transform and probability grids, ROBOT AUT S, 37(1), 2001, pp. 1-18
Citations number
18
Language
INGLESE
art.tipo
Article
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
ROBOTICS AND AUTONOMOUS SYSTEMS
ISSN journal
0921-8890 → ACNP
Volume
37
Issue
1
Year of publication
2001
Pages
1 - 18
Database
ISI
SICI code
0921-8890(20011031)37:1<1:RMRLFS>2.0.ZU;2-W
Abstract
We present a robust position-tracking method for a mobile robot with seven sonar sensors. The method is based on Hough transform and probability grids . The focus of the paper is on the problem of how to handle sparse sensors and noisy data in order to develop a low-cost navigation system for real-wo rld applications. The proposed method consists of three steps. It computes a two-dimensional feature space by applying a straight-line Hough transform to the sonar readings. The detected features are then matched with the wor ld map as reference pattern. The correlation counts obtained in the previou s step are used for updating the position probability grid. We demonstrate that this method, on the one hand, avoids the common problems of feature de tection in sonar data such as erroneous lines through separate clusters, co rner inference, and line artefacts through reflection. On the other hand, i t achieves a robustness that dense sensor-matching techniques, such as Mark ov localisation, can only deliver if they use a complex sensor model which takes into account the angle to the object reflecting the sonar beam. (C) 2 001 Elsevier Science B.V. All rights reserved.