» Download Article

Authors
Gupta, Vishisht; Brennan, Sean

Identifier
10.3182/20070820-3-US-2918.00011

Index Terms
terrain aided localization,inertial navigation,Kalman filter

Abstract
A novel method for estimating vehicle roll, pitch and yaw using machine vision and inertial sensors is presented that is based on matching images captured from an on-vehicle camera to a rendered representation of the surrounding terrain obtained from an on-board map database. United States Geographical Survey Digital Elevation Maps (DEMs) were used to create a 3D topology map of the geography surrounding the vehicle, and it is assumed in this work that large segments of the surrounding terrain are visible, particularly the horizon lines. The horizon lines seen in the captured video from the vehicle are compared to the horizon lines obtained from a rendered geography, allowing absolute comparisons between rendered and actual scene in roll, pitch and yaw. A kinematic Kalman filter modeling an inertial navigation system then uses the scene matching to generate filtered estimates of orientation. Experiments using an instrumented vehicle operating at the test track of the Pennsylvania Transportation Institute were performed to check the validity of the method, and the results reveal a very close match between the vision-based estimates of orientation versus those from a high-quality GPS/INS system.

References

[1] Allan, D. W. (1966). Statistics of atomic frequency standards. In:
    Proceedings of the IEEE. Vol. 52. pp. 221-230.
[2] David M. Bevly (2004). Global positioning system (GPS): A low-cost
     velocity sensor for correcting inertial sensor errors on ground
    vehicles. JDSMC pp. 255-264.
[3] Brad Grinstead, Andreas Koschan, David Page Andrei Gribok and
    Mongi A. Abidi (2005). Vehicle-borne scanning for detailed 3d
    terrain model generation. In: SAE ComVec. Chicago, IL.
[4] Brown, Robert Grover and Patrick Y. C. Hwang (1992). Introduction
    to Random Signals and Applied Kalman Filtering. John Wiley and
    Sons.
[5] Chroust, S.G. and M. Vincze (2004). Fusion of vision and inertial
    data for motion and structure estimation. Journal of Robotics
    Systems 21(2), 73-83.
[6] Chung Hakyoung, L. Ojeda and J. Borenstein (2001). Sensor fusion
    for mobile robot dead-reckoning with a precision-calibrated optic
    gyroscope. IEEE ICRA 1, 3588-3593.
[7] D. Gebre-Egziabher, R.C.Hayward, J.D. Powell (2004). Design
    of multi-sensor attitude determination systems. IEEE TAES
    40(2), 627-649.
[8] Daniel L. Edwards, Gregory B. Desmond and Michael W. Schoppmann
     (1988). Terrain data base generation for autonomous land
    vehicle navigation. Photogrammetria 43(2), 101-107.
[9] Grey Denism and Dale Roberts (1989). Recent developments in
    TERPROM. In: Advances in Techniques and Technologies for Air
    Vehicle Navigation and Guidance.
[10] Dorobantu, R. and C. Gerlach (2004). Investigation of a navigation-grade
      rlg simu type inav-rqh. Technical report. Institut für Astronomische
      und Physikalische Geodäsie Technische Universität
     München. München, Germany.
[11] Ernst, M. D. and B. E. Flinchbaugh (1989). Image/map correspondence
      using curve matching. Technical Report CSC-SIUL-89-12.
     Texas Instruments.
[12] Feizhou Zhang, Xiuwan Chen, Min Sun Ming Yan and Dongkai
     Yang (2004). Simulation design of underwater terrain matching
     navigation based on information fusion. In: IGARSS. Vol. 5.
     pp. 3114-3117.
[13] Fischler, Martin A. and Robert C. Bolles (1981). Random sample
     consensus: A paradigm for model fitting with applications to
     image analysis and automated cartography. In: Communications
     of the ACM. Vol. 24. pp. 381-395.
[14] Forsyth, David A. and Jean Ponce (2003). Computer Vision: A
     Modern Approach. Prentice Hall.
[15] Freeman, H. and S. P. Morse (1967). On searching a contour map
     for a given terrain elevation profile. Journal of Franklin Institute
     284, 1-25.
[16] Fruh, C. and A. Zakhor (2001). Fast 3D model generation in urban
     environments. IEEE MFI pp. 165-170.
[17] Furst, Simon and Ernst-Dieter Dickmanns (1999). A vision based
     navigation system for autonomous aircraft. Robotics and Autonomous
      Systems pp. 173-184.
[18] G. Dissanayake, S. Sukkarieh, E. Nebot and H. Durrant-Whyte
     (2001). The aiding of a low-cost strapdown inertial measurement
     unit using vehicle model constraints for land vehicle applications.
     IEEE TRA 17(5), 731-747.
[19] Gelb, A., Ed. (1974). Applied Optimal Estimation. M.I.T. Press.
     Cambridge, Mass.
[20] Heikkila, Janne and Olli Silven (1997). A four-step camera calibration
      procedure with implicit image correction. In: Proceedings of
     the IEEE CVPR. San Juan, PR, USA. pp. 1106-1112.
[21] Hoffman, Brian D. (1999). Improved rover state estimation in
     challenging terrain. Autonomous Robots 6, 113-130.
[22] L.D. Hostetler and R.D. Andreas (1983). Nonlinear Kalman filter
     techniques for terrain aided navigation. IEEE TAC 3, 315-322.
[23] Hu, Zhencheng and Keiichi Uchimura (2004). Real-time data fusion
     on stabilizing camera pose estimation output for vision-based
     road navigation. In: Proceedings Stereoscopic Displays and Virtual
     Reality Systems. Vol. 5291. pp. 480-490.
[24] Jeffrey J. Rodriguez, J. K. Aggarwal (1990). Matching aerial images
     to 3-D terrain. IEEE TPAMI.
[25] L. Lucido, J. Opderbecke, V. Rigaud R. Deriche and Z. Zhang
     (1996). A terrain referenced underwater positioning using sonar
     bathymetric profiles and multiscale analysis. In: IEEE MTS.
     Vol. 1. Fort Lauderdale, FL, USA. pp. 417-421.
[26] Madhavan, Raj (2004). Terrain aided localization of autonomous
     vehicle. Automation in Construction pp. 83-100.
[27] Ng, Lawrence C. (1997). Characterization of ring laser gyro performance
      using the allan variance method. AIAA JGCD 20(1), 211-
     214.
[28] O. Strauss, F. Comby and M.J. Aldon (1999). Multibeam sonar
     image matching for terrain-based underwater navigation. In:
     IEEE MTS. Vol. 2. Seattle, WA. pp. 882-887.
[29] P. Golden, Joe (1980). Terrain contour matching (tercom): A cruise
     missile guidance aid. In: Proceedings of the Society of Photo-Optical
      Instrumentation Engineers. Vol. 238. pp. 10-18.
[30] R. Lerner, E. Rivlin and P.H. Rotstein (2004). Pose estimation using
     feature correspondences and DTM. In: IEEE ICIP. Singapore.
     pp. 2603-2606.
[31] Rehbinder, H. and B.K. Ghosh (2001). Rigid body state estimation
     using dynamic vision and inertial sensors. In: IEEE CDC. Vol. 3.
     Orlando, Florida. pp. 2398-2403.
[32] Rogers, Robert M. (2000). Applied Mathematics in Integrated Navigation
      Systems. American Institute of Aeronautics and Astronautics.
     
[33] Ryu, C. and J. C. Gerdes (2002). Vehicle sideslip and roll parameter
     estimation using GPS. In: AVEC. Hiroshima, Japan.
[34] Schwartz, Jacob T. (1987). Identification of partially obscured objects
      in two and three dimensions by matching noisy characteristic
      curves. IJRR 6(2), 29-44.
[35] Sinpyo Hong, Man Hyung Lee, Ho Hwang Chun Sun-Hong Kwon
     and J. L. Speyer (2005). Observability of error states in gps/ins
     integration. IEEE TVT 54(2), 731-743.
[36] Strelow, Dennis and Sanjiv Singh (2004). Motion estimation from
     image and inertial measurements. IJRR 23(12), 1157-1195.
[37] Talluri, Raj and J. K. Aggarwal (1992). Position estimation for an
     autonomous mobile robot in an outdoor environment. IEEE TRA
     8(5), 573-584.
[38] Website, USGS. http://seamless.usgs.gov/website/seamless/.
[39] Wolfson, Haim J. (1990). On curve matching. IEEE TPAMI
     12(5), 483-489.
[40] You, S. and U. Neumann (2001). Fusion of vision and gyro tracking
     for robust augmented reality registration. In: Proceedings of the
     IEEE VR. Yokohama, Japan. pp. 71-78.