Abstract
Here we present a robust method for monocular visual odometry capable of accurate position estimation even when operating in undulating terrain. Our algorithm uses a steering model to separately recover rotation and translation. Robot 3DOF orientation is recovered by minimizing image projection error, while, robot translation is recovered by solving an NP-hard optimization problem through an approximation. The decoupled estimation ensures a low computational cost. The proposed method handles undulating terrain by approximating ground patches as locally flat but not necessarily level, and recovers the inclination angle of the local ground in motion estimation. Also, it can automatically detect when the assumption is violated by analysis of the residuals. If the imaged terrain cannot be sufficiently approximated by locally flat patches, wheel odometry is used to provide robust estimation. Our field experiments show a mean relative error of less than 1 %.
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References
K. Konolige, M. Agrawal, J. Sol, Large-scale visual odometry for rough terrain. Robot. Res. 66, 201–212 (2011)
M. Maimone, Y. Cheng, L. Matthies, Two years of visual odometry on the mars exploration rovers. J. Field Robot. 24(2), 169–186 (2007)
D. Nister, O. Naroditsky, J. Bergen, Visual odometry for ground vechicle applications. J. Field Robot. 23(1), 3–20 (2006)
D. Scaramuzza, 1-point-ransac structure from motion for vehicle-mounted cameras by exploiting non-holonomic constraints. Int. J. Comput. Vision. 95, 74–85 (2011)
B. Kitt, J. Rehder, A. Chambers et al., Monocular visual odometry using a planar road model to solve scale ambiguity, in Proceeding European Conference on Mobile Robots, Sept 2011
N. Nourani-Vatani, P. Borges, Correlation-based visual odometry for ground vehicles. J. Field Robot. 28(5), 742–768 (2011)
R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University Press, New York, 2004)
M. Fischler, R. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
A. Howard, Real-time stereo visual odometry for autonomous ground vehicles, in IEEE International Conference on Intelligent Robots and Systems, Nice, France, Sept 2008
D. Dansereau, I. Mahon, O. Pizarro et al., Plenoptic flow: closed-form visual odometry for light field cameras, in International Conference on Intelligent Robots and Systems (IROS), CA, San Francisco, Sept 2011
P. Corke, D. Strelow, S. Singh, Omnidirectional visual odometry for a planetary rover, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems Sendai, Japan, Sept 2004, pp. 149–171
L. Paz, P. Pinies, J. Tardos, Large-scale 6-DOF SLAM with stereo-in-hand. IEEE Trans. Robot. 24(5), 946–957 (2008)
B. Williams, I. Reid, On combining visual slam and visual odometry, in IEEE International Conference on Robotics and Automation, Anchorage, Alaska, May 2010
M. Wongphati, N. Niparnan, A. Sudsang, Bearing only fast SLAM using vertical line information from an omnidirectional camera, in Proceedings of the IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, Feb 2009, pp. 494–501
A. Pretto, E. Menegatti, M. Bennewitz et al., A visual odometry framework robust to motion blur, in IEEE International Conference on Robotics and Automation, Kobe, Japan, May 2009
J. Civera, D. Bueno, A. Davison, J. Montiel, Camera self-calibraction for sequential bayesian structure form motion, in Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan, May 2009, pp. 130–134
D. Scaramuzza, Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints, in IEEE International Conference on Computer Vision, Kyoto, Japan, Sept 2009
T. Gillespie, Fundamentals of Vehicle Dynamics (SAE, International, 1992)
D. Bertsekas, Nonlinear Programming (MA, Cambridge, 1999)
J. Zhang, D. Song, On the error analysis of vertical line pair-based monocular visual odometry in urban area, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, Oct 2009, pp. 187–191
R. Krauthgamer, J. Naory, R. Schwartzz, Partitioning graphs into balanced components, in The Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, NY, New York, Jan 2009
K. Andreev, H. Racke, Balanced graph partitioning. Theory Comput. Syst. 39, 929–939 (2006)
B. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in Proceedings of Imaging Understanding, Workshop, 1981, pp. 121–130
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Zhang, J., Singh, S., Kantor, G. (2014). Robust Monocular Visual Odometry for a Ground Vehicle in Undulating Terrain. In: Yoshida, K., Tadokoro, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40686-7_21
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DOI: https://doi.org/10.1007/978-3-642-40686-7_21
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