Abstract
The problem of estimating and predicting the absolute camera pose (the position and orientation of a camera with respect to the world coordinate system) is approached by fusion of measurements from inertial sensors (accelerometers and gyroscopes) and robot control system. The sensor fusion approach described in this paper is based on non-linear filtering of multi-rate extended Kalman filter. In this way, camera pose estimates, with improved accuracy and sampling rate as well as reduced computation complexity, are available. Experiments that an industrial robot moves the sensors (camera and inertial measurement unit) in an indoor-global positioning system (GPS)-based global referencing system are presented. The absolute camera pose, provided by indoor-GPS, allows for a performance evaluation. The experimental results confirm also the dynamics improvement of the estimated absolute camera pose.
Similar content being viewed by others
References
Bay H, Ess A, Tuytelaars T, Gool LV (2008) Surf: speeded up robust features. Comput Vis Image Und 110(3):346–359
Beyer L (2004) Genauigkeitssteigerung von Industrierobotern, Insbesondere mit Parallelkinematik. Ph.D. thesis, University of the Federal Armed Forces Hamburg
Corke P, Lobo J, Dias J (2007) An introduction to inertial and visual sensing. Int J Robot Res 26(6):519–535
Gatla CS, Lumia R, Wood J, Starr G (2007) An automated method to calibrate industrial robots using a virtual closed kinematic chain. IEEE Trans Robot 23(6):1105–1116
Grigorescu SM, Macesanu G, Cocias TT, Puiu D, Moldoveanu F (2011) Robust camera pose and scene structure analysis for service robotics. Robot Auton Syst 59:899–909
Harris C, Stephens M (1988) A combined corner and edge detector. Proceedings of the fourth Alvery vision conference, pp 147–151
Hartley R, Zisserman A (2004) Multiple view geometry in computer vision, 2 edn. Cambridge University Press, Cambridge
Hol JD (2011) Sensor fusion and calibration of inertial sensors, vision, Ultra-Wideband and GPS. Ph.D. thesis, Linkping University
Hol JD, Schoen TB, Gustafsson F (2010) Modeling and calibration of inertial and vision sensors. Int J Robot Res 29(2):231–244
Kahl F, Agarwal S, Chandraker MK, Kriegman D, Belongie S (2008) Practical global optimization for multiview geometry. Int J Comput Vis 79:271–284
Kailath T, Sayed AH, Hassibi B (2000) Linear estimation. Prentice Hall, New York
Kelly J, Sukhatme GS (2011) Visual-inertial sensor fusion: localization, mapping and sensor-to-sensor self-calibration. Int J Robot Res 30(1):56–79
Kragic D, Christensen HI (2005) Advances in robot vision. Robot Auton Syst 51:1–3
Kuipers JB (1999) Quaternions and rotation sequences: a primer with applications to orbits, aerospace, and virtual reality. Princeton University Press, New York
Lin WY, Cheong LF, Tan P, Dong G, Liu S (2012) Simultaneous camera pose and correspondence estimation with motion coherence. Int J Comput Vis 96:145–161
Lobo J, Dias J (2007) Relative pose calibration between visual and inertial sensors. Int J Robot Res 26(6):561–575
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Ma Y, Soatto S, Kosecka J, Sastry S (2003) An invitation to 3-D vision. From images to geometric models. Springer, New York
Maybeck PS (1979) Stochastic models, estimation, and control, vol 1. Academic Press, New York
Mirzaei FM, Roumeliotis SI (2008) A Kalman filter-based algorithm for IMU-camera calibration: observability analysis and performance evaluation. IEEE Trans Robot 24(5):1143–1156
Moons T, Gool LV, Vergauwen M (2009) 3D reconstruction from multiple images, Part 1: principles. Now Publishers Inc, Hanover
Mueller T, Schwendemann J (2009) iGPS—ein vielseitiges Messsystem hoher Genauigkeit. Allgemeine Vermessungs-Nachrichten 2009(04):146–157
Nister D (2004) An efficient solution to the five-point relative pose problem. IEEE Trans Pattern Anal Mach Intell 26(6):756–777
Norman A, Schoenberg A, Gorlach I, Schmitt R (2010) Cooperation of industrial robots with indoor-GPS. Proceedings of the international conference on competitive manufacturing pp 215–224
Pfeifer T, Schmitt R (2010) Fertigungsmesstechnik, 3 edn. Oldenbourg, Germany
Robertson DP, Cipolla R (2009) Structure from Motion. Varga M (ed) Practical Image Processing and Computer Vision. John Wiley, New York
Schmitt R, Schoenberg A, Damm B (2010) Indoor-GPS based robots as a key technology for versatile production. Proceedings for the joint conference of ISR 2010 (41st international symposium on robotics) and ROBOTIK 2010 (6th German conference on robotics), pp 109–205
Shuster MD (1993) A survey of attitude representations. J Astronaut Sci 41(4):439–517
Strobl KH, Hirzinger G (2006) Optimal hand-eye calibration. Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp 4647–4653
Titterton DH, Weston JL (1997) Strapdown inertial navigation technology (IEE radar, sonar, navigation and avionics series). Peter Peregrinus Ltd, London
Triggs B, McLauchlan P, Hartley R, Fitzgibbon A (1999) Bundle adjustment—a modern synthesis. ICCV ’99: proceedings of the international workshop on vision algorithms, pp 298–372
Tsai R, Lenz R (1989) A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. Trans Robot Autom 5(3):345–358
Woodman O (2007) An introduction to inertial navigation. Technical Report UCAM-CLTR-696. University of Cambridge, Cambridge
Acknowledgments
The authors are very grateful to the DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) for financial supporting this work as part of the research project (SCMH-1856/23-1) "Scene recognition with monocular moving camera in industrial robotics". The authors are also very grateful to the anonymous reviewers for their valuable comments, which helped us to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Schmitt, R., Cai, Y. & Jatzkowski, P. Estimation of the absolute camera pose for environment recognition of industrial robotics. Prod. Eng. Res. Devel. 7, 91–100 (2013). https://doi.org/10.1007/s11740-012-0436-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11740-012-0436-0