Cited By
View all- Hoang VLe MJo K(2014)Hybrid cascade boosting machine using variant scale blocks based HOG features for pedestrian detectionNeurocomputing10.1016/j.neucom.2013.12.017135:C(357-366)Online publication date: 5-Jul-2014
Combining edge and one-point RANSAC algorithm to estimate visual odometry
Pages 556 - 565
Abstract
In recent years, classical structure from motion based SLAM has achieved significant results. Omnidirectional camera-based motion estimation has become interested researchers due to the lager field of view. This paper proposes a method to estimate the 2D motion of a vehicle and mapping by using EKF based on edge matching and one point RANSAC. Edge matching based azimuth rotation estimation is used as pseudo prior information for EKF predicting state vector. In order to reduce requirement parameters for motion estimation and reconstruction, the vehicle moves under nonholonomic constraints car-like structured motion model assumption. The experiments were carried out using an electric vehicle with an omnidirectional camera mounted on the roof. In order to evaluate the motion estimation, the vehicle positions were compared with GPS information and superimposed onto aerial images collected by Google map API. The experimental results showed that the method based on EKF without using prior rotation information given error is about 1.9 times larger than our proposed method.
References
[1]
Fraundorfer, F., Scaramuzza, D.: Visual Odometry: Part II: Matching, Robustness, Optimization, Applications. IEEE Robotics & Automation Magazine 19, 78-90 (2012).
[2]
Grasa, O.G., Civera, J., Montiel, J.M.M.: EKF Monocular SLAM with Relocalization for Laparoscopic Sequences. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4816-4821 (2011).
[3]
Nistér, D., Naroditsky, O., Bergen, J.: Visual Odometry for Ground Vehicle Applications. Journal of Field Robotics 23, 3-20 (2006).
[4]
Royer, E., Lhuillier, M., Dhome, M., Lavest, J.-M.: Monocular Vision for Mobile Robot Localization and Autonomous Navigation. Int. J. Comput. Vis. 74, 237-260 (2007).
[5]
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. Journal of Robotics 13 (2012).
[6]
Konolige, K., Agrawal, M., Solà, J.: Large-Scale Visual Odometry for Rough Terrain. In: Kaneko, M., Nakamura, Y. (eds.) Robotics Research, vol. 66, pp. 201-212 (2011).
[7]
Gandhi, T., Trivedi, M.: Parametric Ego-Motion Estimation for Vehicle Surround Analysis Using an Omnidirectional Camera. Machine Vision and Applications 16, 85-95 (2005).
[8]
Scaramuzza, D.: 1-Point-RANSAC Structure from Motion for Vehicle-Mounted Cameras by Exploiting Non-holonomic Constraints. Int. J. Comput. Vis. 95, 74-85 (2011).
[9]
Scaramuzza, D., Siegwart, R.: Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles. IEEE Transactions on Robotics 24, 1015-1026 (2008).
[10]
Tardif, J.P., Pavlidis, Y., Daniilidis, K.: Monocular Visual Odometry in Urban Environments Using an Omnidirectional Camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), pp. 2531-2538 (2008).
[11]
Caron, F., Duflos, E., Pomorski, D., Vanheeghe, P.: GPS/IMU Data Fusion Using Multisensor Kalman Filtering: Introduction of Contextual Aspects. Information Fusion 7, 221- 230 (2006).
[12]
Kim, S., Yoon, K., Lee, D., Lee, M.: The Localization of a Mobile Robot Using a Pseudolite Ultrasonic System and a Dead Reckoning Integrated System. International Journal of Control, Automation and Systems 9, 339-347 (2011).
[13]
Wei, L., Cappelle, C., Ruichek, Y., Zann, F.: GPS and Stereovision-Based Visual Odometry: Application to Urban Scene Mapping and Intelligent Vehicle Localization. International Journal of Vehicular Technology 17 (2011).
[14]
Suzuki, T., Kitamura, M., Amano, Y., Hashizume, T.: 6-DOF Localization for A Mobile Robot Using Outdoor 3D Voxel Maps. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pp. 5737-5743 (2010).
[15]
Yekeun, J., Yunsu, B., Jun-Sik, K., In-So, K.: Complementation of Cameras and Lasers for accurate 6D SLAM: From Correspondences to Bundle Adjustment. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3581-3588 (2011).
[16]
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 60, 91-110 (2004).
[17]
Mei, C., Rives, P.: Single View Point Omnidirectional Camera Calibration from Planar Grids. In: IEEE International Conference on Robotics and Automation, pp. 3945-3950 (2007).
[18]
Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile Robots. Bradford Company (2004).
[19]
Labrosse, F.: The visual compass: Performance and Limitations of an Appearance-Based Method. Journal of Field Robotics 23, 913-941 (2006).
[20]
Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and Chamfer Matching: Two New Techniques for Image Matching. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, vol. 2, pp. 659-663. Morgan Kaufmann Publishers Inc., Cambridge (1977).
[21]
Hoang, V.-D., Vavilin, A., Jo, K.-H.: Fast Human Detection Based on Parallelogram Haar-Like Feature. In: The 38th Annual Conference of the IEEE Industrial Electronics Society (2012).
[22]
Solà, J., Vidal-Calleja, T., Civera, J., Montiel, J.: Impact of Landmark Parametrization on Monocular EKF-SLAM with Points and Lines. Int. J. Comput. Vis. 97, 339-368 (2012).
[23]
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. MIT Press, Cambridge (2005).
[24]
Civera, J., Grasa, O.G., Davison, A.J., Montiel, J.M.M.: 1-Point RANSAC for Extended Kalman Filtering: Application to Real-Time Structure from Motion and Visual Odometry. J. Field Robot. 27, 609-631 (2010).
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Published In
July 2013
650 pages
ISBN:9783642394782
Sponsors
- National Science Foundation of China
- INNS: International Neural Network Society
- IEEE Computational Intelligence Society: IEEE Computational Intelligence Society
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 28 July 2013
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Cited By
View all- Hoang VLe MJo K(2014)Hybrid cascade boosting machine using variant scale blocks based HOG features for pedestrian detectionNeurocomputing10.1016/j.neucom.2013.12.017135:C(357-366)Online publication date: 5-Jul-2014