Multisensor on-the-fly localization: : Precision and reliability for applications

This paper presents an approach for localization using geometric features from a 360 laser range finder and a monocular vision system. Its practicability under conditions of continuous localization during motion in real time (referred to as on-the-fly localization) is investigated in large-scale experiments. The features are infinite horizontal lines for the laser and vertical lines for the camera. They are extracted using physically well-grounded models for all sensors and passed to a Kalman filter for fusion and position estimation. Positioning accuracy close to subcentimeter has been achieved with an environment model requiring 30 bytes/m 2 . Already with a moderate number of matched features, the vision information was found to further increase this precision, particularly in the orientation. The results were obtained with a fully self-contained system where extensive tests with an overall length of more than 6.4 km and 150,000 localization cycles have been conducted. The final testbed for this localization system was the Computer 2000 event, an annual computer tradeshow in Lausanne, Switzerland, where during 4 days visitors could give high-level navigation commands to the robot via a web interface. This gave us the opportunity to obtain results on long-term reliability and verify the practicability of the approach under application-like conditions. Furthermore, general aspects and limitations of multisensor on-the-fly localization are discussed. © 2001 Elsevier Science B.V. All rights reserved.

[1]  Javier González,et al.  Two-dimensional landmark-based position estimation from a single image , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[2]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[3]  Y. Bar-Shalom Tracking and data association , 1988 .

[4]  Nicola Tomatis,et al.  Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision , 1999, 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99). Proceedings (Cat. No.99EX355).

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

[6]  Roland Siegwart,et al.  Feature extraction and scene interpretation for map-based navigation and map building , 1998, Other Conferences.

[7]  J.-S. Gutmann,et al.  AMOS: comparison of scan matching approaches for self-localization in indoor environments , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[8]  Kai Oliver Arras,et al.  The need for autonomy and real-time in mobile robotics: a case study of XO/2 and Pygmalion , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[9]  Günther Schmidt,et al.  Fusing range and intensity images for mobile robot localization , 1999, IEEE Trans. Robotics Autom..

[10]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[11]  James L. Crowley,et al.  Position estimation for a mobile robot using vision and odometry , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[12]  J. M. M. Montiel,et al.  Continuous mobile robot localization: vision vs. laser , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[13]  G. F. McLean,et al.  Line-Based Correction of Radial Lens Distortion , 1997, CVGIP Graph. Model. Image Process..

[14]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[15]  Wolfram Burgard,et al.  An experimental comparison of localization methods , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

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

[17]  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.

[18]  R. Simmons,et al.  Probabilistic Navigation in Partially Observable Environments , 1995 .

[19]  James L. Crowley World modeling and position estimation for a mobile robot using ultrasonic ranging , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[20]  Lindsay Kleeman,et al.  Accurate odometry and error modelling for a mobile robot , 1997, Proceedings of International Conference on Robotics and Automation.

[21]  Illah R. Nourbakhsh,et al.  DERVISH - An Office-Navigating Robot , 1995, AI Mag..