Abstract
This paper describes a novel image-based method for robot orientation estimation based on a single omnidirectional camera. The estimation of orientation is computed by finding the best pixel-wise match between images as a function of the rotation of the second image. This is done either using the first image as the reference image or with a moving reference image. Three datasets were collected in different scenarios along a “Gummy Bear” path in outdoor environments. This carefully designed path has the appearance of a gummy bear in profile, and provides many curves and sets of image pairs that are challenging for visual robot localisation. We compare our method to a feature-based method using SIFT and another appearance-based visual compass. Experimental results demonstrate that the appearance-based methods perform well and more consistently than the feature based method, especially when the compared images were grabbed at positions far apart.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Cao, J., Labrosse, F., Dee, H.: A novel image similarity measure for place recognition in visual robotic navigation. In: Herrmann, G., Studley, M., Pearson, M., Conn, A., Melhuish, C., Witkowski, M., Kim, J.-H., Vadakkepat, P. (eds.) TAROS-FIRA 2012. LNCS, vol. 7429. Springer, Heidelberg (2012)
Fernández, L., Payá, L., Reinoso, Ó., Amorós, F.: Appearance-based visual odometry with omnidirectional images - a practical application to topological mapping. In: Proceedings of ICINCO, vol. 2, pp. 205–210 (2011)
García, D.V., Rojo, L.F., Aparicio, A.G., Castelló, L.P., García, O.R.: Visual odometry through appearance- and feature-based method with omnidirectional images. J. Robot. 2012, 1–13 (2012)
Goecke, R., Asthana, A., Pettersson, N., Petersson, L.: Visual vehicle egomotion estimation using the Fourier-Mellin transform. In: IEEE Intelligent Vehicles Symposium, pp. 450–455 (2007)
Labrosse, F.: The visual compass: performance and limitations of an appearance-based method. J. Field Robot. 23(10), 913–941 (2006)
Lourenco, M., Barreto, J.P., Vasconcelos, F.: sRD-SIFT: keypoint detection and matching in images with radial distortion. IEEE Trans. Robot. 28(3), 752–760 (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Matthies, L., Shafer, S.: Error modeling in stereo navigation. IEEE J. Robot. Autom. 3(3), 239–250 (1987)
Milford, M.J., Wyeth, G.F.: Single camera vision-only slam on a suburban road network. In: Proceedings of ICRA, pp. 3684–3689 (2008)
Moravec, H.: Obstacle avoidance and navigation in the real world by a seeing robot rover. Technical report CMU-RI-TR-80-03, Robotics Institute, Carnegie Mellon University (1980)
Nistr, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. J. Field Robot. 23(1), 3–20 (2006)
Olson, C.F., Matthies, L.H., Schoppers, M., Maimone, M.W.: Rover navigation using stereo ego-motion. Robot. Auton. Syst. 43(4), 215–229 (2003)
Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. Robot. Autom. Mag. 18(4), 80–92 (2011)
Tomasi, C., Shi, J.: Direction of heading from image deformations. In: Proceedings of CVPR, pp. 422–427 (1993)
Woodland, A., Labrosse, F.: On the separation of luminance from colour in images. In: Proceedings of VVG, pp. 29–36 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, J., Labrosse, F., Dee, H. (2014). An Evaluation of Image-Based Robot Orientation Estimation. In: Natraj, A., Cameron, S., Melhuish, C., Witkowski, M. (eds) Towards Autonomous Robotic Systems. TAROS 2013. Lecture Notes in Computer Science(), vol 8069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43645-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-662-43645-5_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43644-8
Online ISBN: 978-3-662-43645-5
eBook Packages: Computer ScienceComputer Science (R0)