Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T18:57:05.225Z Has data issue: false hasContentIssue false
  • English
  • Français

Mirage: an O(n) time analytical solution to 3D camera pose estimation with multi-camera support

Published online by Cambridge University Press:  16 February 2017

Semih Dinc Open the ORCID record for Semih Dinc [Opens in a new window] ,
Farbod Fahimi  and
Ramazan Aygun Open the ORCID record for Ramazan Aygun [Opens in a new window]
Semih Dinc*
Affiliation:
Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabamaaygunr@uah.edu
Farbod Fahimi
Affiliation:
Mechanical & Aerospace Engineering, University of Alabama in Huntsville, Huntsville, Alabama. E-mail: ff0002@uah.edu
Ramazan Aygun
Affiliation:
Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabamaaygunr@uah.edu
*
*Corresponding author. E-mail: sd0016@uah.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Mirage is a camera pose estimation method that analytically solves pose parameters in linear time for multi-camera systems. It utilizes a reference camera pose to calculate the pose by minimizing the 2D projection error between reference and actual pixel coordinates. Previously, Mirage has been successfully applied to trajectory tracking (visual servoing) problem. In this study, a comprehensive evaluation of Mirage is performed by particularly focusing on the area of camera pose estimation. Experiments have been performed using simulated and real data on noisy and noise-free environments. The results are compared with the state-of-the-art techniques. Mirage outperforms other methods by generating fast and accurate results in all tested environments.

Keywords

Camera pose estimationPerspective-N-Point (PnP)Object localizationAnalytical solutionO(n) time complexity
Type
Articles
Information
Robotica , Volume 35 , Issue 12 , December 2017 , pp. 2278 - 2296
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Petersen, T., A Comparison of 2D-3D Pose Estimation Methods Master's Thesis (Lautrupvang: Aalborg University-Institute for Media Technology Computer Vision and Graphics, 2008).Google Scholar
2. Nöll, T., Pagani, A. and Stricker, D., “Real-Time Camera Pose Estimation using Correspondences with High Outlier Ratios,” VISAPP 2010: International Conference on Computer Vision Theory and Applications, Angers, France (2010) pp. 381–386.Google Scholar
3. Jaramillo, C., Dryanovski, I., Valenti, R. G. and Xiao, J., “6-DOF Pose Localization in 3D Point-Cloud Dense Maps Using a Monocular Camera,” Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE, Shenzhen, China, (2013) pp. 1747–1752.Google Scholar
4. Ferraz, L., Binefa, X. and Moreno-Noguer, F., “Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Columbus, Ohio, USA, (2014) pp. 501–508.Google Scholar
5. Tron, R., Zhou, X. and Daniilidis, K., “A Survey on Rotation Optimization in Structure from Motion,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, Nevada, USA (2016) pp. 77–85.Google Scholar
6. Ansar, A. and Daniilidis, K., “Linear pose estimation from points or lines,” Pattern IEEE Trans. Anal. Mach. Intell. 25 (5), 578–589 (2003).Google Scholar
7. Lepetit, V., Moreno-Noguer, F. and Fua, P., “EPnP: An Accurate O(n) Solution to the PnP Problem,” Int. J. Comput. Vis. 81 (2), 155–166 (2009).Google Scholar
8. Kneip, L., Furgale, P. and Siegwart, R., “Using Multi-Camera Systems in Robotics: Efficient Solutions to the nPnP Problem,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE, Karlsruhe, Germany, (2013) pp. 3770–3776.Google Scholar
9. Chen, C. and Schonfeld, D., “Robust 3D pose estimation from multiple video cameras,” Proceedings of the 16th IEEE International Conference on Image Processing (ICIP), IEEE, Cairo Egypt (2009) pp. 541–544.Google Scholar
10. Stewenius, H., Engels, C. and Nistér, D., “Recent developments on direct relative orientation,” ISPRS J. Photogramm. Remote Sens. 60 (4), 284–294 (2006).Google Scholar
11. Lee, G. H., Li, B., Pollefeys, M. and Fraundorfer, F., “Minimal Solutions for Pose Estimation of a Multi-Camera System,” Proceedings of the International Symposium on Robotics Research (ISRR), Singapore (2013) pp. 1–16.Google Scholar
12. Chang, W. Y. and Chen, C. S., “Pose estimation for multiple camera systems,” Proceedings of the 17th International Conference on Pattern Recognition, ICPR, vol. 3, IEEE, Cambridge, UK (2004) pp. 262–265.Google Scholar
13. Dinc, S., Fahimi, F. and Aygun, R., “Vision-based trajectory tracking for mobile robots using mirage pose estimation method,” IET Computer Vision (Institution of Engineering and Technology) 10 (5), 450–458 (2016).Google Scholar
14. Dinc, S., Fahimi, F. and Aygun, R., “Vision-Based Trajectory Tracking Approach for Mobile Platforms in 3D World using 2D Image Space,” Proceedings of the ASME International Mechanical Engineering Congress and Exposition, (IMECE), vol. 4 B, San Diego, CA, United States (2013).Google Scholar
15. Leonard, S., Learning Feed-Forward Control for Vision-Guided Robotics PhD Thesis (University of Alberta, Computing Science, Alberta, Canada, 2008).Google Scholar
16. Chum, O. and Matas, J., “Matching with Prosac - Progressive Sample Consensus,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, vol. 1, San Diego, CA, USA, (2005) pp. 220–226.Google Scholar
17. Rosenhahn, B., Pose Estimation Revisited PhD Thesis (Inst. für Informatik und Praktische Mathematik, Kiel, Germany, 2003).Google Scholar
18. Grest, D., Petersen, T. and Krüger, V., “A Comparison of Iterative 2D-3D Pose Estimation Methods for Real-Time Applications,” In: Image Analysis (Salberg, A., Hardeberg, J. Y. and Jenssen, R., eds.), (Springer, Oslo, Norway, 2009) pp. 706–715.Google Scholar
19. Nöll, T., Pagani, A. and Stricker, D., “Markerless Camera Pose Estimation-an Overview,” In: OASIcs-OpenAccess Series in Informatics (Middel, A., Scheler, I. and Hagen, H., eds.) vol. 19 (Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 2011) pp. 45–54.Google Scholar
20. Besl, P. J. and McKay, H. D., “A method for registration of 3-d shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14 (2), 239–256 (1992).Google Scholar
21. Dementhon, D. F. and Davis, L. S., “Model-based object pose in 25 lines of code,” Int. J. Comput. Vis. 15 (1–2), 123–141 (1995).Google Scholar
22. Guo, Y., “A novel solution to the p4p problem for an uncalibrated camera,” J. Math. Imaging Vis. 45 (2), 186–198 (2013).Google Scholar
23. Tang, J., Chen, W.-S. and Wang, J., “A novel linear algorithm for P5P problem,” Appl. Math. Comput. 205 (2), 628–634 (2008).Google Scholar
24. Zheng, Y., Kuang, Y., Sugimoto, S., Astrom, K. and Okutomi, M., “Revisiting the pnp Problem: A Fast, General and Optimal Solution,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, Sydney, Australia (2013) pp. 2344–2351.Google Scholar
25. Lu, C.-P., Hager, G. D. and Mjolsness, E., “Fast and globally convergent pose estimation from video images,” IEEE Trans. Pattern Anal. Mach. Intell. 22 (6), 610–622 (2000).Google Scholar
26. Vandenhouten, R., Kistel, T. and Wendlandt, O., “A method for optical indoor localization of mobile devices using multiple identifiable landmarks,” Trans. IoT Cloud Comput. 1 (1) 1–10 (2015).Google Scholar
27. Quan, L. and Lan, Z., “Linear n-point camera pose determination,” IEEE Trans. Pattern Anal. Mach. Intell. 21 (8), 774–780 (1999).Google Scholar
28. Choi, C. and Christensen, H. I., “3D Pose Estimation of Daily Objects using An rgb-d Camera,” Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Vilamoura, Portugal (2012) pp. 3342–3349.Google Scholar
29. Szeliski, R., Computer Vision: Algorithms and Applications (Springer Science & Business Media, Springer-Verlag, NY, USA 2010).Google Scholar
30. Hesch, J., Roumeliotis, S., “A Direct Least-Squares (DLS) Method for PnP,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, Barcelona, Spain (2011) pp. 383–390.Google Scholar
31. Li, S., Xu, C. and Xie, M., “A robust O(n) solution to the perspective-n-point problem,” IEEE Trans. Pattern Anal. Mach. Intell. 34 (7), 1444–1450 (2012).CrossRefGoogle Scholar
32. Fabian, J. and Clayton, G., “Error analysis for visual odometry on indoor, wheeled mobile robots with 3-d sensors, Mechatronics,” IEEE/ASME Trans. 19 (6), 1896–1906 (2014).Google Scholar
33. Dryanovski, I., Valenti, R. G. and Xiao, J., “Fast Visual Odometry and Mapping from RGB-D Data,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE, Shenzhen, China (2013) pp. 2305–2310.Google Scholar
34. Svarm, L., Enqvist, O., Oskarsson, M. and Kahl, F., “Accurate Localization and Pose Estimation for Large 3D Models,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, (2014) pp. 532–539.Google Scholar
35. Engels, C., Stewénius, H. and Nistér, D., “Bundle adjustment rules,” Photogramm. Comput. Vis. 2 124–131 (2006).Google Scholar