Similar articles

Abstract: We present an algorithm which can realize 3D stereo vision simultaneous localization and mapping (SLAM) for mobile robot in unknown outdoor environments, which means the 6-DOF motion and a sparse but persistent map of natural landmarks be constructed online only with a stereo camera. In mobile robotics research, we extend FastSLAM 2.0 like stereo vision SLAM with “pure vision” domain to outdoor environments. Unlike popular stochastic motion model used in conventional monocular vision SLAM, we utilize the ideas of structure from motion (SFM) for initial motion estimation, which is more suitable for the robot moving in large-scale outdoor, and textured environments. SIFT features are used as natural landmarks, and its 3D positions are constructed directly through triangulation. Considering the computational complexity and memory consumption, Bkd-tree and Best-Bin-First (BBF) search strategy are utilized for SIFT feature descriptor matching. Results show high accuracy of our algorithm, even in the circumstance of large translation and large rotation movements.

[1] Arulampalam, M.S., Maskell, S., Gordon, N., 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 50(2):174-188.

[2] Beis, J.S., Lowe, D.G., 1997. Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.1000-1006.

[3] Davison, A.J., 2003. Real-Time Simultaneous Localisation and Mapping with a Single Camera. Proc. Ninth IEEE Int. Conf. on Computer Vision, p.1403-1410.

[4] Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O., 2007. MonoSLAM: real-time single camera SLAM. IEEE Trans. on Pattern Anal. Machine Intell., 29(6):1052-1067.

[5] Eade, E., Drummond, T., 2006. Scalable Monocular SLAM. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.469-476.

[6] Elinas, P., Sim, R., Little, J.J., 2006. ( SLAM: Stereo Vision Slam Using the Rao-Blackwellised Particle Filter and a Novel Mixture Proposal Distribution. Proc. IEEE Int. Conf. on Robotics and Automation, p.1564-1570.

[7] Hartley, R., Zisserman, A., 2003. Mutiple View Geometry in Computer Vision. Cambridge University Press.

[8] Horn, B., 1987. Closed-form solution of absolute orientation using unit quaternion. J. Opt. Soc. Am., 4:629-642.

[9] Jensfelt, P., Kragic, D., Folkesson, J., Bjorkman, M., 2006. A Framework for Vision Based Bearing Only 3D SLAM. Proc. IEEE Int. Conf. on Robotics and Automation, p.1944-1950.

[10] Julier, S., Uhlmann, J., 1996. A General Method for Approximating Nonlinear Transformations of Probability Distributions. Technical Report, Department of Engineering Science, University of Oxford. Http://www.robots.ox.ac.uk/siju/work/publications/lettersize/Unscented.zip

[11] Laurent, I., 2006. The iLab Neuromorphic Vision C++ Toolkit. University of Southern California. Http://ilab.usc.edu/toolkit/home.shtml

[12] Lowe, D.G., 2004. Distinctive image features from scale-invariant keypoints. Int. J. Computer Vision, 60(2):91-110.

[13] Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., 2003. FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges. Proc. Int. Joint Conf. on Artificial Intelligence, p.1151-1156.

[14] Murphy, K., 1999. Bayesian Map Learning in Dynamic Environments. In: Advances in Neural Information Processing Systems. MIT Press, Denver, USA, p.1015-1021.

[15] Nister, D., Naroditsky, O., Bergen, J., 2004. Visual Odometry. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.652-659.

[16] Procopiuc, O., Agarwal, P., Arge, L., Vitter, J.S., 2002. Bkd-tree: A Dynamic Scalable Kd-tree. In: Advances in Spatial and Temporal Databases (SSTD). Springer Press, Berlin/Heidelberg, 2750:46-65.

[17] Robinson, J.T., 1981. The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes. Proc. Int. Conf. on Management of Data, p.10-18.

[18] Sim, R., Elinas, P., Griffin, M., Little, J.J., 2005. Vision-based SLAM Using the Rao-Blackwellised Particle Filter. Proc. IJCAI Workshop on Reasoning with Uncertainty in Robotics, p.9-16.

[19] Vergauwen, M., Pollefeys, M., Goll, L.V., 2003. A stereo-vision system for support of planetary surface exploration. Machine Vision and Applications, 14(1):5-14.