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Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

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Abstract

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans.

In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.

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References

  1. Besl, P. J. and McKay, N. D.: A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2) (1992), 239–256.

    Google Scholar 

  2. Black, M. J.: Robust incremental optical flow, PhD Thesis, Yale University, September 1992.

  3. Blais, G. and Levine, M. D.: Registering multiple range data to create 3D-computer objects, IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8) (1992), 820–824.

    Google Scholar 

  4. Chen, Y. and Medioni, G.: Object modelling by registration of multiple range images, International Journal of Image and Vision Computing 10(3) (1992), 145–155.

    Google Scholar 

  5. Cox, I. J.: Blanche: Position estimation for an autonomous robot vehicle, in: Autonomous Robot Vehicles, I. J. Cox and G. T. Wilfong (eds), Springer-Verlag, 1990.

  6. Cox, I. J.: Banche: An experiment in guidance and navigation of an autonomous robot vehicle, IEEE Transactions on Robotics and Automation 7(2) (1991), 193–204.

    Google Scholar 

  7. Cox, I. J. and Kruskal, J. B.: On the congruence of noisy images to line segments models, in: Second International Conference on Computer Vision, 1988, pp. 252–258.

  8. Crowley, J. L.: World modeling and position estimation for a mobile robot using ultrasonic ranging, in: IEEE International on Robotics Conference on Robotics and Automation, 1989, pp. 674–680.

  9. Fennema, C., hanson, A., Riseman, E., Beveridge, J. R. and Kumar, R.: Model-directed mobile robot navigation, IEEE Transaction on Systems, Man, and Cybernetics 20(6) (1990), 1352–1369.

    Google Scholar 

  10. Grimson, W. E. L., Lozano-Pérez, T. and Huttenlocher, D. P.: Object Recognition by Computer: The Role of Geometric Constraints, MIT Press, Cambridge, MA, 1990.

    Google Scholar 

  11. Habert, O. and Pruski, A.: Dynamic modelling of an indoor environemnt, in: IEEE International Conference on Robotics and Automation, Minneapolis, 1996, pp. 751–756.

  12. Hampel, F. R., Ronchetti, E. M. Rousseeuw, P. J. and Stahel, W. A.: Robust Statistics: The Approach Based on Influence Functions, John Wiley and Sons, New York, 1986.

    Google Scholar 

  13. Hebert, P., Betge-Brezetz, S. and Chatila, R.: Decoupling odometry and exteroceptive perception in building a global world map of a mobile robot: The sue of local maps, in: IEEE International Conference on Robotics and Automation, Minneapolis, 1996, pp. 757–764.

  14. Higuchi, K., Hebert, M. and Ikeuchi, K.: Building 3-D models from unregistered range images, in: IEEE International Conference on Robotics and Automation, 1994, pp. 2248–2253.

  15. Iyengar, S. and Elfs, A.: Autonomous Mobile Robots, Vols 1 and 2, IEEE Computer Society Press, Los Alamitos, CA, 1991.

    Google Scholar 

  16. Kahaner, D., Moler, C. and Nash, S.: Numerical Methods and Software, Prentice Hall, Englewood Cliffs, NJ, 1989.

    Google Scholar 

  17. Kalvin, A., Sconberg, E., Schwartz, J. T. and Sharir, M.: Two-dimensional, model-based, boundary matching using footprints, International Journal of Robotics Research 5(4) (1986), 38–55.

    Google Scholar 

  18. Kosaka, A. and Kak, A. C.: Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties, CVGIP: Image Understanding 56(3) (1992), 271–329.

    Google Scholar 

  19. Kuipers, B. and Byun, Y.: A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations, Robotics and Autonomous Systems 8(1991), 47–63.

    Google Scholar 

  20. Latombe, J-C.: Robot Motion Planning, Kluwer Academic Publishers, Boston, MA, 1991.

    Google Scholar 

  21. Leonard, J., Durrant-Whyte, H. and Cox. I. J.: Dynamic map building for an autonomous mobile robot, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 1990.

  22. Levitt, T. S. and Lawton, D. T.: Qualitative navigation for mobile robots, Artificial Intelligence 44(1990), 305–360.

    Google Scholar 

  23. Lu, F.: Shape registration using optimization for mobile robot navigation, PhD Thesis, University of Toronto, Department of Computer Science, 1995.

  24. Lu, F. and Milios, E.: Optimal global pose estimation for consistent sensor data registration, in: IEEE International Conference on Robotics and Automation, Nagoya, Japan, 1995, pp. 93–100.

  25. Lumelsky, V.: A comparative study of the path length performance of maze-searching and robot motion algorithms, IEEE Transations on Robotics and Automation 7(1) (1991).

  26. MacKenzie, P. and Dudek, G.: Precise positioning using model-based maps, in: IEEE International Conference on Robotics and Automation, 1994.

  27. Nickerson, S. B., Jenkin, M., Milios, E., Down, B., Jasiobedzki, P., Jepson, A., Terzopoulos, D., Tsotsos, J., Wilkes, D., Bains, N. and Tran. K.: Design of ARK, a sensor-based mobile robot for industrial environments, in: INTELLIGENT VEHICLES, Tokyo, Japan, 1993.

  28. Schiele, B. and Crowley, J.: A comparison of position estimating techniques using occupancy grids, Robotics and Autonomous Systems 12(1994), 163–171.

    Google Scholar 

  29. Weiss, G., Wetzler, C. and von Puttkamer, E.: Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, Munchen, Germany, 1994, pp. 595–601.

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Authors and Affiliations

  1. Department of Computer Science, York University, North York, Canada, M3J 1P3

    Feng Lu & Evangelos Milios

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  1. Feng Lu
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  2. Evangelos Milios
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Lu, F., Milios, E. Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. Journal of Intelligent and Robotic Systems 18, 249–275 (1997). https://doi.org/10.1023/A:1007957421070

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