Stereo Visual Odometry for Urban Vehicles Using Ground Features
Autonomous vehicles rely on the accurate estimation of their pose, speed and direction of travel to perform basic navigation tasks. Although GPSs are very useful, they have some drawbacks in urban applications. Visual odometry is an alternative or complementary method, because it uses a sensor already available in many vehicles for other tasks and provides the ego motion of the vehicle with enough accuracy. In this paper, a new method is proposed that detects and tracks features available on the surface of the ground, due to the texture of the road or street and road markings. This way it is assured only static points are taking into account in order to obtain the relative movement between images. A Kalman filter is used taking into account the Ackermann steering restrictions. Some results in real urban environments are shown in order to demonstrate the good performance of the algorithm.
KeywordsAutonomous vehicles Visual odometry
Unable to display preview. Download preview PDF.
- 2.Nister, D., Naroditsky, O., Bergen, J.: Visual odometry. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 652–659. IEEE Press, New York (2004)Google Scholar
- 5.Badino, H., Yamamoto, A., Kanade, T.: Visual odometry by multi-frame feature integration. In: IEEE International Conference on Computer Vision Workshops, pp. 222–229. IEEE Press, New York (2013)Google Scholar
- 6.Lu, W., Xiang, Z., Liu, J.: High-performance visual odometry with two-stage local binocular high-performance visual odometry with two-stage local binocular BA and GPU. In: IEEE Intelligent Vehicles Symposium, pp. 1107–1112. IEEE Press, New York (2013)Google Scholar
- 8.Bellavia, F., Fanfani, M., Pazzaglia, F., Colombo, C.: Robust selective stereo SLAM without loop closure and bundle adjustment. In: Image Analysis and Processing – ICIAP 2013. LNCS, vol. 8156, pp. 462–471. Springer, Heidelberg (2013)Google Scholar
- 9.Sanfourche, M., Vittori, V., Le Besnerais, G.: eVO: a realtime embedded stereo odometry for MAV applications. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2107–2114. IEEE Press, New York (2013)Google Scholar
- 13.Point Cloud Library (PCL). http://pointclouds.org
- 15.Hesch, J.A., Roumeliotis, S.I.: A direct least-squares (DLS) method for PnP. In: IEEE International Conference on Computer Vision, pp. 383–390. IEEE Press, New York (2011)Google Scholar
- 16.Open source Computer Vision. http://Opencv.org
- 17.Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361. IEEE Press, New York (2012)Google Scholar