Abstract
This paper presents an algorithm for moving object detection (MOD) in robot visual simultaneous localization and mapping (SLAM). The algorithm is designed based on the defining epipolar constraint for the corresponding feature points on image plane. An essential matrix obtained using the state estimator is utilized to represent the epipolar constraint. Meanwhile, the method of speeded-up robust feature (SURF) is employed in the algorithm to provide a robust detection for image features as well as a better description of landmarks and of moving objects in visual SLAM system. Experiment is carried out on a hand-held monocular camera to validate the performances of the proposed algorithm. The results show that the integration of MOD and SURF is efficient for robot navigating in dynamic environments.
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References
Wang, C.C., Thorpe, C., Thrun, S., Hebert, M., Durrant-Whyte, H.: Simultaneous localization, mapping and moving object tracking. Int. J. Robot. Res. 26, 889–916 (2007)
Bibby, C., Reid, I.: Simultaneous Localisation and Mapping in Dynamic Environments (SLAMIDE) with Reversible Data Association. In: Proceedings of Robotics: Science and Systems III. Georgia Institute of Technology, Atlanta (2007)
Zhao, H., Chiba, M., Shibasaki, R., Shao, X., Cui, J., Zha, H.: SLAM in a Dynamic Large Outdoor Environment using a Laser Scanner. In: Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, California, pp. 1455–1462 (2008)
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: Real-time single camera SLAM. IEEE T. Pattern Anal. 29, 1052–1067 (2007)
Paz, L.M., Pinies, P., Tardos, J.D., Neira, J.: Large-Scale 6-DOF SLAM with Stereo-in-Hand. IEEE T. Robot. 24, 946–957 (2008)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151. University of Mancheter (1988)
Karlsson, N., Bernardo, E.D., Ostrowski, J., Goncalves, L., Pirjanian, P., Munich, M.E.: The vSLAM Algorithm for Robust Localization and Mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 24–29 (2005)
Sim, R., Elinas, P., Little, J.J.: A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM. Int. J. Comput. Vision 74, 303–318 (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision. MIT Press, Cambridge (2005)
Smith, R., Self, M., Cheeseman, P.: Estimating Uncertain Spatial Relationships in Robotics. In: Cox, I.J., Wilfong, G.T. (eds.) Autonomous Robot Vehicles. Springer, New York (1990)
Blom, A.P., Bar-Shalom, Y.: The interacting multiple-model algorithm for systems with Markovian switching coefficients. IEEE T. Automat. Control 33, 780–783 (1988)
Hutchinson, S., Hager, G.D., Corke, P.I.: A tutorial on visual servo control. IEEE T. Robot. Automat. 12, 651–670 (1996)
Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vision 30, 79–116 (1998)
Wang, Y.T., Lin, M.C., Ju, R.C.: Visual SLAM and Moving Object Detection for a Small-size Humanoid Robot. Int. J. Adv. Robot. Syst. 7, 133–138 (2010)
Civera, J., Davison, A.J., Montiel, J.M.M.: Inverse Depth Parametrization for Monocular SLAM. IEEE Trans. Robot. 24, 932–945 (2008)
Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. Nature 293, 133–135 (1981)
Hartley, R.I.: In Defense of the Eight-Point Algorithm. IEEE T. Pattern Anal. 19, 580–593 (1997)
Luong, Q.T., Faugeras, O.D.: The Fundamental Matrix: Theory, Algorithms, and Stability Analysis. Int. J. Comput. Vision 17, 43–75 (1996)
Wang, Y.T., Sun, C.H., Chiou, M.J.: Detection of Moving Objects in Image Plane for Robot Navigation using Monocular Vision. EURASIP J. Adv. Sig. Pr. 2012, 29 (2012)
Bouguet JY (2012) Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc (accessed March 18, 2012)
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Wang, YT., Chen, KW., Chiou, MJ. (2013). Moving Object Detection Using Monocular Vision. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_17
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DOI: https://doi.org/10.1007/978-3-642-33926-4_17
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