Abstract
In this paper we propose a probabilistic observation model for stereo vision systems which avoids explicit data association between observations and the map by marginalizing the observation likelihood over all the possible associations. We define observations as sets of landmarks composed of their 3D locations, assumed to be normally distributed, and their SIFT descriptors. Our model has been integrated into a particle filter to test its performance in map building and global localization, as illustrated by experiments with a real robot.
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Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50, 174–188 (2002)
Davison, A.J.: Real-Time Simultaneous Localisation and Mapping with a Single Camera. In: Proc. International Conference on Computer Vision, vol. 2, pp. 1403–1410 (2003)
Davison, A.J., Cid, Y.G., Kita, N.: Real-Time 3D SLAM with Wide-Angle Vision. In: 5th Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal (2004)
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation 17, 229–241 (2001)
Harris, C.J., Stephens, M.: A combined edge and corner detector. In: Proceedings of 4th Alvey Vision Conference, Manchester, pp. 147–151 (1988)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)
Julier, S.J., Ulhmann, J.K.: A New Extension of the Kalman Filter to Nonlinear Systems. In: Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, Orlando (1997)
Kitchen, L., Rosenfeld, A.: Gray-level corner detection. Pattern Recognition Letters 1, 95–102 (1982)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Matthies, L., Shafer, S.A.: Error modeling in Stereo Navigation. IEEE Journal of Robotics and Automation 3(3) (1987)
Menegatti, E., Pretto, A., Scarpa, A., Pagello, E.: Omnidirectional Vision Scan Matching for Robot Localization in Dynamic Environments. IEEE Trans. on Robotics 22(3), 523–535 (2006)
Montemerlo, M.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem With Unknown Data Association. PhD Thesis (2003)
Saeedi, P., Lawrence, P.D., Lowe, D.G.: Vision-Based 3-D Trajectory Tracking for Unknown Environments. IEEE Transactions on Robotics 22(1), 119–136 (2006)
Se, S., Lowe, D., Little, J.: Local and Global Localization for Mobile Robots using Visual Landmarks. In: Proc. International Conference on Intelligent Robots and Systems, pp. 414–420 (2001)
Shi, J., Tomasi, C.: Good features to track. In: Proc. Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2006)
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Moreno, F.A., Blanco, J.L., Gonzalez, J. (2007). A Probabilistic Observation Model for Stereo Vision Systems: Application to Particle Filter-Based Mapping and Localization. In: MartÃ, J., BenedÃ, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_45
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DOI: https://doi.org/10.1007/978-3-540-72847-4_45
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