Abstract
This paper presents an approach for egomotion estimation over stereo image sequences combined with extra GPS data. The accuracy of the estimated motion data is tested with 3D roadside reconstruction. Our proposed method follows the traditional flowchart of many visual odometry algorithms: it firstly establishes the correspondences between the keypoints of every two frames, then it uses the depth information from the stereo matching algorithms, and it finally computes the best description of the cameras’ motion. However, instead of simply using keypoints from consecutive frames, we propose a novel technique that uses a set of augmented and selected keypoints, which are carefully tracked by a Kalman filter fusion. We also propose to use the GPS data for each key frame in the input sequence, in order to reduce the positioning errors of the estimations, so that the drift errors could be corrected at each key frame. Finally, the overall growth of the build-up errors can be bounded within a certain range. A least-squares process is used to minimise the reprojection error and to ensure a good pair of translation and rotation measures, frame by frame. Experiments are carried out for trajectory estimation, or combined trajectory and 3D scene reconstruction, using various stereo-image sequences.
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References
Badino, H., Franke, U., Rabe, C., Gehrig, S.: Stereo vision-based detection of moving objects under strong camera motion. In: Proceedings of Computer Vision Theory and Applications, vol. 2, pp. 253–260 (2006)
Badino, H., Kanade, T.: A head-wearable short-baseline stereo system for the simultaneous estimation of structure and motion. In: Proceedings of the IAPR Conference on Machine Vision Applications, pp. 185–189 (2011)
Badino, H., Yamamoto, A., Kanade, T.: Visual odometry by multi-frame feature integration. In: Proceedings of ICCV Workshop on Computer Vision for Autonomous Driving, pp. 222–229 (2013)
Besl, P., McKay, N.D.: A method for registration of 3-d shapes. Proceedings of Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Demirdjian, D., Darrell, T.: Motion estimation from disparity images. Proceedings of ICCV 1, 213–218 (2001)
Franke, U., Rabe, C., Badino, H., Gehrig, S.K.: 6D-Vision: fusion of stereo and motion for robust environment perception. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 216–223. Springer, Heidelberg (2005)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. International Journal of Robotics Research 32(11), 1231–1237 (2013)
Julier, S., Uhlmann, J.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401–422 (2004)
Kitt, B., Geiger, A., Lategahn, H.: Visual odometry based on stereo image sequences with ransac-based outlier rejection scheme. In: Proceedings of Intelligent Vehicles Symposium, pp. 486–492 (2010)
Klette, R.: Concise Computer Vision. Springer, London (2014)
Maimone, M., Cheng, Y., Matthies, L.: Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics 24, 169–186 (2007)
Matthies, L.: Dynamic stereo vision. Ph.D. dissertation, Carnegie Mellon University (1989)
Matthies, L., Shafer, S.A.: Error modeling in stereo navigation. IEEE Journal of Robotics and Automation 3, 239–250 (1987)
Milella, A., Siegwart, R.: Stereo-based ego-motion estimation using pixel tracking and iterative closest point. In: Proceedings of IEEE International Conference on Computer Vision Systems, pp. 21–21 (2006)
Olson, C., Matthies, L., Schoppers, M., Maimone, M.: Stereo ego-motion improvements for robust rover navigation. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 2, pp. 1099–1104 (2001)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: Proceedings of ICCV, pp. 2564–2571 (2011)
Scaramuzza, D., Fraundorfer, F.: Visual odometry tutorial. Robotics Automation Magazine 18(4), 80–92 (2011)
Shakernia, O., Vidal, R., Sastry, S.: Omnidirectional egomotion estimation from back-projection flow. In: Proceedings of CVPR Workshop, vol. 7, pp. 82–82 (2003)
Sibley, G., Sukhatme, G.S., Matthies, L.: The iterated sigma point kalman filter with applications to long range stereo. In: Proceedings of Robotics: Science and Systems (2006)
Song, Z., Klette, R.: Robustness of point feature detection. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 91–99. Springer, Heidelberg (2013)
Tian, T.Y., Tomasi, C., Heeger, D.J.: Comparison of approaches to egomotion computation. In: Proceedings of CVPR, pp. 315–320 (1996)
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Geng, H., Chien, HJ., Nicolescu, R., Klette, R. (2015). Egomotion Estimation and Reconstruction with Kalman Filters and GPS Integration. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_33
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DOI: https://doi.org/10.1007/978-3-319-23192-1_33
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