Abstract
Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ballard, D. H. and Brown, C. M.: Computer Vision, Prentice-Hall, Englewood Cliffs, NJ, 1982.
Bar-Shalom, Y. and Fortmann, T. E.: Tracking and Data Association, Academic Press, Boston, MA, 1988.
Bezdek, J. C.: Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981.
Castellanos, J. A. and Tardos, J. D.: Mobile Robot Localization and Map Building: A Multisensor Fusion Approach, Kluwer Academic, Boston, MA, 1999.
Chong, K. S. and Kleeman, L.: Feature-based mapping in real, large scale environments using an ultrasonic array, Internat. J. Robotics Res. 18(1) (1999), 3-19.
Chong, K. S. and Kleeman, L.: Mobile robot map building from an advanced sonar array and accurate odometry, Internat. J. Robotics Res. 18(1) (1999), 20-36.
Choset, H. and Nagatani, K.: Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization, IEEE Trans. Robotics Automat. 17(2) (2001), 125-137.
Dave, R. N.: Use of adaptive fuzzy clustering algorithm to detect lines in digital images, Intelligent Robots Comput. Vision VIII 1192(2) (1989), 600-611.
Dave, R. N.: Characterization and detection of noise in clustering, Pattern Recognition Lett. 12 (1991), 657-664.
Dempster, A., Laird, A., and Rubin, D.:Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B 39 (1977), 1-38.
Dissanayake, M. W. M. G., Newman, P., Durrant-Whyte, H., Clark, S., and Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem, IEEE Trans. Robotics Automat. 17(3) (2001), 229-241.
Dissanayake, M. W. M. G., Williams, S. B., Durrant-Whyte, H., and Bailey, T.: Map management for efficient simultaneous localization and mapping (SLAM), Autonom. Robots 12 (2002), 267-286.
Fischler, M. A. and Bolles, R. C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Comm. Assoc. Comput. Mach. 24(6) (1981), 381-395.
Guivant, J. E. and Nebot, E. M.: Optimization of the simultaneous localization and map building algorithm for real time implementation, IEEE Trans. Robotics Automat. 17(3) (2001), 242-257.
Gutmann, J. S. and Konolige, K.: Incremental mapping of large cyclic environments, in: Internat. Symposium on Computational Intelligence in Robotics and Automation, CIRA, Monterey, 1999.
Illingworth, J. and Kittler, J.: A survey of the Hough transform, Comput. Vision Graphics Image Process. 44 (1988), 87-116.
Ip, Y. L., Rad, A. B., Chow, K. M., and Wong, Y. K.: Segment-based map building using enhanced adaptive fuzzy clustering algorithm for mobile robot applications, J. Intelligent Robotic Systems 35 (2002), 221-245.
Ip, Y. L., Rad, A. B., and Wong, Y. K.: Map building via integration of fuzzy systems and clustering algorithms, in: Tenth IEEE Internat. Conf. on Fuzzy Systems, FUZZ-IEEE 2001, Melbourne, Australia, 2001, Vol. 3, pp. 1058-1061.
Jetto, L., Longhi, S., and Venturini, G.: Development and experimental validation of an adaptive extended Kalman filter for localization of mobile robots, IEEE Trans. Robotics Automat. 15(2) (1999), 219-229.
Leonard, J. J. and Durrant-Whyte, H. F.: Mobile robot localization by tracking geometric beacons, IEEE Trans. Robotics Automat. 7(3) (1991), 376-382.
Leonard, J. J. and Durrant-Whyte, H. F.: Simultaneous map building and localization for an autonomous mobile robot, in: IEEE/RSJ Internat. Workshop on Intelligent Robots and Systems, IROS, Japan, 1991, Vol. 3, pp. 1442-1447.
Leonard, J. J. and Durrant-Whyte, H. F.: Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic, Boston, MA, 1992.
Leonard, J. J. and Feder, H. J. S.: A computationally efficient method for large-scale concurrent mapping and localization, in: D. Koditschek and J. Hollerbach (eds), Ninth Internat. Symposium in Robotics Research, Snowbird, UT, Springer, 2000, pp. 169-176.
Leonard, J. J., Rikoski, R. J., Newman, P. M., and Bosse, M.: Mapping partially observable features from multiple uncertain vantage points, Internat. J. Robotics Res., in press.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Ip, Y.L., Rad, A.B. Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data. Journal of Intelligent and Robotic Systems 39, 149–172 (2004). https://doi.org/10.1023/B:JINT.0000015402.60437.6a
Issue Date:
DOI: https://doi.org/10.1023/B:JINT.0000015402.60437.6a