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High-Resolution Terrain Map from Multiple Sensor Data
February 1992 (vol. 14 no. 2)
pp. 278-292

The authors present 3-D vision techniques for incrementally building an accurate 3-D representation of rugged terrain using multiple sensors. They have developed the locus method to model the rugged terrain. The locus method exploits sensor geometry to efficiently build a terrain representation from multiple sensor data. The locus method is used to estimate the vehicle position in the digital elevation map (DEM) by matching a sequence of range images with the DEM. Experimental results from large-scale real and synthetic terrains demonstrate the feasibility and power of the 3-D mapping techniques for rugged terrain. In real world experiments, a composite terrain map was built by merging 125 real range images. Using synthetic range images, a composite map of 150 m was produced from 159 images. With the proposed system, mobile robots operating in rugged environments can build accurate terrain models from multiple sensor data.

[1] J. Bares,et al., "An autonomous rover for exploring mars,"Special Issue Comput. Mag. Autonomous Intelligent Machines, vol. 44, pp. 75-145, Oct. 1988.
[2] A. Blake and A. Zisserman,Visual Reconstruction. Cambridge, MA: MIT Press, 1987.
[3] M. Brady, J. Ponce, A. Yuille, and H. Asada, "Describing surfaces," inProc. Second Int. Symp. Robotics Res.(H. Hanafusa and H. Inoue, Eds.). Cambridge, MA: MIT Press, 1985, pp. 5-16.
[4] D. L. Edwards, G. B. Desmond, and M. W. Schoppmann, "Terrain data base generation for autonomous land vehicle navigation,"Photogrammetria, vol. 43, pp. 101-107, Apr. 1988.
[5] D. B. Gennery, "Visual terrain matching for a Mars rover," inProc. 1989 IEEE Conf. Robotics Automat., May 1989.
[6] W. E. L. Grimson, "Computational experiments with a feature-based stereo algorithm,"IEEE Trans. Patt. Anal. Machine Intell., vol. 7, no. 1, pp. 17-34, Jan. 1985.
[7] M. Hebert, C. Cailas, E. Krotkov, I. Kweon, and T. Kanade, "Terrain mapping for a roving planetary explorer," inProc. 1989 IEEE Conf. Robotics and Automat., May 1989.
[8] M. Hebert, T. Kanade, and I. S. Kweon, "3-D vision techniques for autonomous vehicles," Tech. Rep. CMU-RI-TR-88-12, Carnegie-Mellon Univ., Pittsburgh, PA, 1988.
[9] M. G. Kendall and P. A. P. Moran,Geometrical Probabilities. New York: Hafner, 1963.
[10] I. Kweon, "Modeling rugged terrain by mobile robots with multiple sensors," Ph.D. thesis, Carnegie Mellon Univ., Robotics Institute, Pittsburgh, PA, Aug. 1990.
[11] I. Kweon, M. Hebert, and T. Kanade, "Perception for rugged terrain," inProc. SPIE Mobile Robots, (Cambridge, MA), 1988.
[12] I. Kweon, M. Hebert, and T. Kanade, "Sensor fusion of range and reflectance data for outdoor scene analysis," inProc. Space Oper. Automat. Robotics, (Cleveland), 1988.
[13] I. Kweon, R. Hoffman, and E. Krotkov, "Experimental characterization of the perception laser rangefinder," Techn. Rep. CMU-RI-TR-91-1, Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, 1991.
[14] P. M. Lee,Bayesian Statistics: An Introduction. Oxford: Oxford University Press, 1989.
[15] L. Matthies, R. Szeliski, and T. Kanade, "Kalman filter-based algorithms for estimating depth from image sequences," inProc. Image Understanding Workshop, (Cambridge), 1988.
[16] J. E. W. Mayhew and J. P. Frisby, "Psychophysical and computational studies towards a theory of human stereopsis,"Artificial Intell., vol. 17, pp. 349-408, Aug. 1981.
[17] F. H. Moffitt and H. Bouchard.Surveying. New York: Harper Row, 1982.
[18] F. R. Norvelle, "Interactive digital correlation techniques for automatic compilation of elevation data," inProc. ASP/ACSM Conf., Feb. 1981.
[19] K. E. Olin, M. Daily, J. Harris, and F. M. Vilnrotter, "Knowledge-based vision technology overview for obstacle detection and avoidance," inProc. IU Workshop, 1989.
[20] K. S. Roberts, "A new representation for a line," inProc. Comput. Vision," Patt. Recogn., Ann Arbor, MI, 1988.
[21] R. S. Szeliski, "Bayesian modeling of uncertainty in low-level vision," Ph.D. dissertation, Carnegie-Mellon Univ., Computer Science Dept., Pittsburgh, PA, Aug. 1988.
[22] R. Szeliski, "Estimating motion from sparse range data without correspondence,"2nd Int. Conf. Comput. Vision(Tarpon Springs, FL), Dec. 5-8, 1988, pp. 207-216.
[23] D. Terzopoulos, "Multiresolution computation of visible-surface representations" Ph.D. thesis, Mass. Inst. Technol., Cambridge, 1984.
[24] H. J. Thomas, D. S. Wettergreen, C. E. Thorpe, and R. M. Hoffman, "Simulation of the Ambler environment," inProc. the 23rd Pittsburgh Conf. Modeling Simulation, (Pittsburgh), 1990.

Index Terms:
3D vision; robot vision; computerised navigation; terrain map; multiple sensor data; locus method; sensor geometry; digital elevation map; synthetic range images; mobile robots; terrain models; computational geometry; computer vision; computerised navigation; computerised pattern recognition; mobile robots
I.S. Kweon, T. Kanade, "High-Resolution Terrain Map from Multiple Sensor Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 278-292, Feb. 1992, doi:10.1109/34.121795
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