Real Time Monocular Visual Odometry Using Hybrid Features and Distance Ratio for Scale Estimation

Diky Septa Nugroho, Igi Ardiyanto, Adha Imam Cahyadi

Abstract


Real time dead reckoning navigation is important for supplying information of the current position of an autonomous mobile robot to complete its task, especially in certain areas such as hazardous and GPS-denied areas. Monocular visual odometry is a good choice as it is one of the dead reckoning navigation method which only uses single camera. For real time task, visual odometry requires fast feature extraction without ignoring its accuracy. Therefore, we propose the usage of a hybrid feature, i.e. Censure feature detector and upright SURF feature descriptor, as feature extraction. Yet, the scale ambiguity for the monocular visual odometry becomes a challenging problem. Without additional information from other sensors, estimating the scale is solely the only way. In our proposed work, distance ratio is employed to tackle such problems. Experimental results show the performance of the designed algorithm. A real example of running the proposed algorithm under an embedded device is also provided for demonstrating its real time capability.

Keywords


visual odometry; distance ratio; scaling factor; Censure; upright SURF.

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References


D. Scaramuzza and F. Fraundorfer, ”Visual Oodometry: Part I: The First 30 Years and Fundamentals,” IEEE Robotics & Automation Magazine, vol 18, no. 4, Dec.2011, pp. 80-92.

H. Yu, H. Sun, Y. Wang, L. Kan and Q. Wan, "An improved visual odometry optimization algorithm based on Kinect camera," 2013 Chinese Automation Congress, Changsha, 2013, pp. 691-696.

T. Kanade, “Transforming camera geometry to a virtual downwardlooking camera: robust ego-motion estimation and ground-layer detection,” in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. IEEE Comput. Soc, 2003, pp. I–390–I–397.

S. Lovegrove, A. J. Davison, and J. Ibanez-Guzman, “Accurate visual odometry from a rear parking camera,” in 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, Jun. 2011, pp. 788–793.

Y. Yu, C. Pradalier, and G. Zong, “Appearance-based monocular visual odometry for ground vehicles,” 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 862–867, Jul. 2011.

A. Cumani, “Feature Localization Refinement for Improved Visual Odometry Accuracy,” Int. Jour. Circuits, Systems and Signal Processing, vol. 5, no. 2, pp. 151–158, 2011.

D. Nist´er, O. Naroditsky, and J. Bergen, “Visual odometry,” in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., vol. 1. Ieee, 2004, pp. 652–659.

B. Kitt, A. Geiger, and H. Lategahn, “Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme,” in 2010 IEEE Intelligent Vehicles Symposium. IEEE, Jun. 2010, pp. 486–492.

A. Geiger, J. Ziegler, and C. Stiller, “Stereoscan: Dense 3d reconstruction in real-time,” in Intelligent Vehicles Symposium (IV), 2011 IEEE, pp. 963–968.

S. Choi, J. Park and W. Yu, "Resolving scale ambiguity for monocular visual odometry," 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju, 2013, pp. 604-608.

F. Fraundorfer, D. Scaramuzza and M. Pollefeys, "A constricted bundle adjustment parameterization for relative scale estimation in visual odometry," 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1899-1904.

A. T. N. Nishitani and D. F. Wolf, "Solving the Monocular Visual Odometry Scale Problem with the Efficient Second-Order Minimization Method," 2015 12th Latin American Robotics Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR), Uberlandia, 2015, pp. 126-131.

D. Zhou, Y. Dai, and H. Li, "Reliable scale estimation and correction for monocular Visual Odometry," 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, 2016, pp. 490-495.

M. Agrawal, K. Konolige, and M. Blas, “Censure: Center surround extremas for realtime feature detection and matching,” in Proc. European Conf. Computer Vision, 2008, pp. 102–115.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” Computer Vision and Image Understanding (CVIU), vol. 110, no. 3, pp. 346–359, 2008.

P. Viola and M. Jones, “Robust real-time face detection,” In: ICCV 2001.

H. Longuet-Higgins, “A computer algorithm for reconstructing a scene from two projections,” Nature, vol. 293, no. 10, pp. 133–135, 1981.

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.

M.A. Fischler and R. C. Bolles,” Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communication of the ACM 24 (1981), 381-395.




DOI: http://dx.doi.org/10.18517/ijaseit.8.5.3247

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