Abstract
In this paper we present a minimal solution for the rotational alignment of IMU-camera systems based on a homography formulation. The image correspondences between two views are related by homography when the motion of the camera can be effectively approximated as a pure rotation. By exploiting the rotational angles of the features obtained by e.g. the SIFT detector, we compute the rotational alignment of IMU-camera systems with only 1 feature correspondence. The novel minimal case solution allows us to cope with feature mismatches efficiently and robustly within a random sample consensus (RANSAC) scheme. Our method is evaluated on both synthetic and real scene data, demonstrating that our method is suited for the rotational alignment of IMU-camera systems.
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Notes
- 1.
k-quantiles divide an ordered data set into k regular intervals.
References
Barath, D.: Five-point fundamental matrix estimation for uncalibrated cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 235–243 (2018)
Bender, D., Schikora, M., Sturm, J., Greniers, D.: Ins-camera calibration without ground control points. In: 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1–6 (2014)
Daniilidis, K.: Hand-eye calibration using dual quaternions. Int. J. Robot. Res. 18(3), 286–298 (1999)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Fraundorfer, F., Tanskanen, P., Pollefeys, M.: A minimal case solution to the calibrated relative pose problem for the case of two known orientation angles. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 269–282. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_20
Guan, B., Vasseur, P., Demonceaux, C., Fraundorfer, F.: Visual odometry using a homography formulation with decoupled rotation and translation estimation using minimal solutions. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2320–2327 (2018)
Guan, B., Yu, Q., Fraundorfer, F.: Minimal solutions for the rotational alignment of imu-camera systems using homography constraints. Comput. Vis. Image Underst. 170, 79–91 (2018)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Heller, J., Havlena, M., Pajdla, T.: Globally optimal hand-eye calibration using branch-and-bound. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 1027–1033 (2016)
Horaud, R., Dornaika, F.: Hand-eye Calibration. Int. J. Robot. Res. 14(3), 195–210 (1995)
Hwangbo, M., Kim, J.S., Kanade, T.: Gyro-aided feature tracking for a moving camera: fusion, auto-calibration and GPU implementation. Int. J. Robot. Res. 30(14), 1755–1774 (2011)
Karpenko, A., Jacobs, D., Baek, J., Levoy, M.: Digital video stabilization and rolling shutter correction using gyroscopes. CSTR 1, 2 (2011)
Kelly, J., Sukhatme, G.S.: Visual-inertial sensor fusion: localization, mapping and sensor-to-sensor self-calibration. Int. J. Robot. Res. 30(1), 56–79 (2011)
Kneip, L., Chli, M., Siegwart, R.Y.: Robust real-time visual odometry with a single camera and an IMU. In: Proceedings of the British Machine Vision Conference (2011)
Kukelova, Z., Bujnak, M., Pajdla, T.: Automatic generator of minimal problem solvers. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 302–315. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88690-7_23
Kukelova, Z., Heller, J., Pajdla, T.: Hand-eye calibration without hand orientation measurement using minimal solution. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7727, pp. 576–589. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37447-0_44
Lowe, D.G., Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Mirzaei, F.M., Roumeliotis, S.I.: A Kalman filter-based algorithm for IMU-camera calibration: observability analysis and performance evaluation. IEEE Trans. Rob. 24(5), 1143–1156 (2008)
Park, F.C., Martin, B.J.: Robot sensor calibration: solving ax = xb on the Euclidean group. IEEE Trans. Robot. Autom. 10(5), 717–721 (1994)
Ruland, T., Pajdla, T., Krüger, L.: Globally optimal hand-eye calibration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1035–1042 (2012)
Saurer, O., Vasseur, P., Boutteau, R., Demonceaux, C., Pollefeys, M., Fraundorfer, F.: Homography based egomotion estimation with a common direction. IEEE Trans. Pattern Anal. Mach. Intell. 39, 327–341 (2016)
Seo, Y., Choi, Y.J., Lee, S.W.: A branch-and-bound algorithm for globally optimal calibration of a camera-and-rotation-sensor system. In: Proceedings of the International Conference on Computer Vision, pp. 1173–1178 (2009)
Tsai, R.Y., Lenz, R.K.: A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Trans. Robot. Autom. 5(3), 345–358 (1989)
Weiss, S., Achtelik, M.W., Chli, M., Siegwart, R.: Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV. In: 2012 IEEE International Conference on Robotics and Automation, pp. 31–38 (2012)
Zhang, Z.Q.: Cameras and inertial/magnetic sensor units alignment calibration. IEEE Trans. Instrum. Meas. 65(6), 1495–1502 (2016)
Zheng, Y., Kuang, Y., Sugimoto, S., Astrom, K., Okutomi, M.: Revisiting the PnP problem: a fast, general and optimal solution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2344–2351 (2013)
Zhuang, H., Shiu, Y.C.: A noise tolerant algorithm for wrist-mounted robotic sensor calibration with or without sensor orientation measurement. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 1095–1100 (1992)
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Guan, B., Su, A., Li, Z., Fraundorfer, F. (2019). Rotational Alignment of IMU-camera Systems with 1-Point RANSAC. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_15
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