Abstract
This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint detection and line extraction. In data pre-processing, low-level data organization and processing are discussed, with emphasis to sensor bias compensation. Breakpoint detection allows to determine sequences of measurements which are not interrupted by scanning surface changing. Two breakpoint detectors are investigated, one based on adaptive thresholding, and the other on Kalman filtering. Implementation and tuning of both detectors are also investigated. Line extraction is performed to each continuous scan sequence in a range image by applying line kernels. We have investigated two classic kernels, commonly used in mobile robots, and our Split-and-Merge Fuzzy (SMF) line extractor. SMF employs fuzzy clustering in a split-and-merge framework without the need to guess the number of clusters. Qualitative and quantitative comparisons using simulated and real images illustrate the main characteristics of the framework when using different methods for breakpoint and line detection. These comparisons illustrate the characteristics of each estimator, which can be exploited according to the platform computing power and the application accuracy requirements.
Similar content being viewed by others
References
Arras, K. O., Tomatis, N., Jensen, B. T. and Siegwart, R.: 2001, Multisensor on-the-fly localization: Precision and reliability for applications, Robotics and Autonomous Systems 34, 131–143.
Barni, M., Cappellini, V., Paoli, A. and Mecocci, A.: 1996, Unsupervised detection of straight lines through possibilistic clustering, in: IEEE International Conference on Image Processing, pp. 963–966.
Bar-Shalom, Y. and Fortmann, T. E.: 1987, Tracking and Data Association, Academic Press, London, U.K.
Bezdek, J. C.: 1981, Pattern Recognition With Fuzzy Objective Function Algorithms, Plenum Press, New York.
Borenstein, J., Everett, H. R. and Feng, L.: 1996, Navigating Mobile Robots: Systems and Techniques, A. K. Peters, Wellesley, MA.
Borges, G. A. and Aldon, M.-J.: 2000, A Split-and-Merge segmentation algorithm for line extractions in 2-D range images, in: Proc of 15th International Conference on Pattern Recognition.
Borges, G. A., Aldon, M.-J. and Gil, T.: 2001, An optimal pose estimator for map-based mobile robot dynamic localization: Experimental comparison with the EKF, in: IEEE International Conference on Robotics and Automation.
Borges, G. A. and Aldon, M.-J.: 2001, Design of a robust real-time dynamic localization system for mobile robots, in: 9th International Symposium on Intelligent Robotic Systems, Toulouse, France.
Borges, G. A. and Aldon, M.-J.: 2002, A decoupled approach for simultaneous stochastic mapping and mobile robot localization, in: IEEE/RSJ International Conference on Intelligent Robots and Systems.
Borges, G. A. and Aldon, M.-J.: 2003, Robustified estimation algorithms for mobile robot localization based on geometrical environment maps, Robotics and Autonomous Systems 45(3–4), 131–159.
Castellanos, J. A. and Tardós, J. D.: 1996, Laser-based segmentation and localization for a mobile robot, in: M. Jamshidi, F. Pin and P. Dauchez (eds.), Robotics and Manufacturing: Recent Trends in Research and Applications, Vol. 6, ASME Press.
Davé, R. N. and Krishnapuram, R.: 1997, Robust clustering methods: A unified view, IEEE Transactions on Fuzzy Systems 5(7), 270–293.
Duda, R. O. and Hart, P. E.: 1973, Pattern Classification and Scene Analysis, Wiley, New York.
Einsele, T.: 1997, Real-time self-localization in unknown indoor environments using a panorama laser range finder, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 697–702.
Forsberg, J., Larsson, U. and Wernersson, Å.: 1995, Mobile robot navigation using the rangeweighted Hough transform, IEEE Robotics and Automation Magazine, 18–26.
Frigui, H. and Krishnapuram, R.: 1999, A robust competitive clustering algorithm with applications in computer vision, IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 450–465.
Haralick, R. M.: 1994, Propagating covariances in computer vision, in: International Conference on Pattern Recognition, pp. 493–498.
Jazwinski, A. H.: 1970, Stochastic Processes and Filtering Theory, Academic Press, New York.
Jensfelt, P. and Christensen, H. I.: 1998, Laser based position acquisition and tracking in an indoor environment, in: International Symposium on Robotics and Automation, pp. 331–338.
Jolion, J. M., Meer, P. and Bataouche, S.: 1991, Robust clustering with applications in computer vision, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 791–802.
Kämpke, T. and Strobel, M.: 2001, Polygonal model fitting, J. Intelligent Robotic Systems 30, 279–310.
Kwon, Y. D. and Lee, J. S.: 1999, A stochastic environment map building method for mobile robot using 2-D laser range finder, Autonomous Robots 7, 187–200.
Pears, N. E.: 2000, Feature extraction and tracking for scanning range sensors, Robotics and Autonomous Systems 33, 43–58.
Peña, J. M., Lozano, J. A. and Larrañaga, P.: 1995, An empirical comparison of four initialization methods for the K-means algorithm, Pattern Recognition Letters 20, 1027–1040.
Shpilman, R. and Brailovsky, V.: 1999, Fast and robust techniques for detecting straight line segments using local models, Pattern Recognition Letters 20(8), 865–877.
Siadat, A. and Dufaut, M.: 1998, Real time and dynamic local map building by using a 2D laser scanner, in: AVCS, pp. 307–312.
Siadat, A., Kaske, A., Klausmann, S., Dufaut, M. and Husson, R.: 1997, An optimized segmentation method for a 2D laser-scanner applied to mobile robot navigation, in: 3rd IFAC Symp. on Intelligent Components and Instruments for Control Applications, France, pp. 153–158.
Skrzypczynski, P.: 1995, Building geometrical map of environment using IR range finder data, in: Intelligent Autonomous Systems, pp. 408–412.
Skrzypczynski, P.: 1997, Environment modelling using optical scanner data, in: IFAC Symposium on Robot Control, pp. 187–192.
Vandorpe, J., van Brussel, H. and Xu, H.: 1996, Exact dynamic map building for a mobile robot using geometrical primitives produced by a 2D range finder, in: IEEE International Conference on Robotics and Automation, Minneapolis, MN, pp. 901–908.
Young, T. Y. and Fu, K.-S.: 1986, Handbook of Pattern Recognition and Image Processing, Academic Press, London, U.K.
Zhang, L. and Ghosh, B. K.: 2000, Line segment based map building and localization using 2D laser rangefinder, in: IEEE International Conference on Robotics and Automation, pp. 2538–2543.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Borges, G.A., Aldon, MJ. Line Extraction in 2D Range Images for Mobile Robotics. Journal of Intelligent and Robotic Systems 40, 267–297 (2004). https://doi.org/10.1023/B:JINT.0000038945.55712.65
Issue Date:
DOI: https://doi.org/10.1023/B:JINT.0000038945.55712.65