Improved Data Association for ICP-based Scan Matching in Noisy and Dynamic Environments

This paper presents a technique to improve the data association in the iterative closest point based scan matching. The method is based on a distance-filter constructed on the basis of an analysis of the set of solutions produced by the associations in the sensor configuration space. This leads to a robust strategy to filter all the associations that do not explain the principal motion of the scan (due to noise in the sensor, large odometry errors, spurious, occlusions or dynamic features for example). The experimental results suggest that the improvement of the data association leads to more robust and faster methods in the presence of wrong correspondences.

[1]  A.-J. Baerveldt,et al.  Localization in changing environments by matching laser range scans , 1999, 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99). Proceedings (Cat. No.99EX355).

[2]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Luis Montesano,et al.  An architecture for sensor-based navigation in realistic dynamic and troublesome scenarios , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[4]  Wolfram Burgard,et al.  An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[5]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[6]  Luis Montesano,et al.  Lessons Learned in Integration for Sensor-Based Robot Navigation Systems , 2006 .

[7]  Luis Montesano,et al.  Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[10]  Roland Siegwart,et al.  Scan alignment with probabilistic distance metric , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[11]  Sebastian Thrun,et al.  Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Rafael Gutiérrez,et al.  Direct motion estimation from a range scan sequence , 1999, J. Field Robotics.

[13]  Simon Lacroix,et al.  Autonomous Rover Navigation on Unknown Terrains: Functions and Integration , 2000, Int. J. Robotics Res..

[14]  Pavel Krsek,et al.  The Trimmed Iterative Closest Point algorithm , 2002, Object recognition supported by user interaction for service robots.

[15]  Riccardo Poli,et al.  Robust mobile robot localisation from sparse and noisy proximity readings using Hough transform and probability grids , 2001, Robotics Auton. Syst..

[16]  Lina María Paz,et al.  Global localization in SLAM in bilinear time , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[18]  Luis Montesano,et al.  Probabilistic scan matching for motion estimation in unstructured environments , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Florent Lamiraux,et al.  Metric-based iterative closest point scan matching for sensor displacement estimation , 2006, IEEE Transactions on Robotics.

[20]  José A. Castellanos,et al.  Constraint-based mobile robot localization , 1996 .

[21]  Ingemar J. Cox,et al.  Blanche-an experiment in guidance and navigation of an autonomous robot vehicle , 1991, IEEE Trans. Robotics Autom..

[22]  Stergios I. Roumeliotis,et al.  Weighted range sensor matching algorithms for mobile robot displacement estimation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).