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JRM Vol.29 No.2 pp. 365-380
doi: 10.20965/jrm.2017.p0365
(2017)

Paper:

ORB-SHOT SLAM: Trajectory Correction by 3D Loop Closing Based on Bag-of-Visual-Words (BoVW) Model for RGB-D Visual SLAM

Zheng Chai and Takafumi Matsumaru

Graduate School of Information, Production and Systems, Waseda University
2-7 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan

Received:
August 1, 2016
Accepted:
November 14, 2016
Published:
April 20, 2017
Keywords:
trajectory correction, loop closing, bag-of-visual-words (BoVW), 3D vocabulary, RGB-D SLAM
Abstract

ORB-SHOT SLAM: Trajectory Correction by 3D Loop Closing Based on Bag-of-Visual-Words (BoVW) Model for RGB-D Visual SLAM

Visual odometry + trajectory correction

This paper proposes the ORB-SHOT SLAM or OS-SLAM, which is a novel method of 3D loop closing for trajectory correction of RGB-D visual SLAM. We obtain point clouds from RGB-D sensors such as Kinect or Xtion, and we use 3D SHOT descriptors to describe the ORB corners. Then, we train an offline 3D vocabulary that contains more than 600,000 words by using two million 3D descriptors based on a large number of images from a public dataset provided by TUM. We convert new images to bag-of-visual-words (BoVW) vectors and push these vectors into an incremental database. We query the database for new images to detect the corresponding 3D loop candidates, and compute similarity scores between the new image and each corresponding 3D loop candidate. After detecting 2D loop closures using ORB-SLAM2 system, we accept those loop closures that are also included in the 3D loop candidates, and we assign them corresponding weights according to the scores stored previously. In the final graph-based optimization, we create edges with different weights for loop closures and correct the trajectory by solving a nonlinear least-squares optimization problem. We compare our results with several state-of-the-art systems such as ORB-SLAM2 and RGB-D SLAM by using the TUM public RGB-D dataset. We find that accurate loop closures and suitable weights reduce the error on trajectory estimation more effectively than other systems. The performance of ORB-SHOT SLAM is demonstrated by 3D reconstruction application.

References
  1. [1] A. J. Davison and I. D. Reid, “MonoSLAM: Real-time single camera SLAM,” IEEE Trans. on pattern analysis and machine intelligence, Vol.29, No.6, pp. 1052-1067, 2007.
  2. [2] F. Endres, J. Hess, J. Sturm, D. Cremers, and W. Burgard, “3-D mapping with an RGB-D camera,” IEEE Trans. on Robotics, Vol.30, No.1, pp. 177-187, 2014.
  3. [3] R. Mur-Artal, J. M. M. Montiel, and J. D. Tardós, “Orb-slam: a versatile and accurate monocular slam system,” IEEE Trans. on Robotics, Vol.31, No.5, pp. 1147-1163, 2015.
  4. [4] H. Hagiwara, Y. Touma, K. Asami, and M. Komori, “FPGA-Based Stereo Vision System Using Gradient Feature Correspondence,” J. of Robotics and Mechatronics, Vol.27, No.6, pp. 681-690, 2015.
  5. [5] T. Suzuki, Y. Amano, T. Hashizume, and S. Suzuki, “3D terrain reconstruction by small Unmanned Aerial Vehicle using SIFT-based monocular SLAM,” J. of Robotics and Mechatronics, Vol.23, No.2, pp. 292-301, 2011.
  6. [6] A. Sujiwo, T. Ando, E. Takeuchi, Y. Ninomiya, and M. Edahiro, “Monocular Vision-Based Localization Using ORB-SLAM with LIDAR-Aided Mapping in Real-World Robot Challenge,” J. of Robotics and Mechatronics, Vol.28, No.4, pp. 479-490, 2016.
  7. [7] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” 2011 IEEE Int. Conf. on computer vision, 2011.
  8. [8] V. Lepetit, F. Moreno-Noguer, and P. Fua, “Epnp: An accurate o(n) solution to the pnp problem,” Int. J. of computer vision, Vol.81, No.2, pp. 155-166, 2009.
  9. [9] D. Galvez-Lopez, and J. D. Tardos, “Real-time loop detection with bags of binary words,” 2011 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2011.
  10. [10] S. Aoyagi, A. Kohama, Y. Inaura, M. Suzuki, and T. Takahashi, “Image-Searching for Office Equipment Using Bag-of-Keypoints and AdaBoost,” J. of Robotics and Mechatronics, Vol.23, No.6, pp. 1080-1090, 2011.
  11. [11] R. Mur-Artal and J. D. Tardós, “Fast relocalisation and loop closing in keyframe-based SLAM,” 2014 IEEE Int. Conf. on Robotics and Automation (ICRA), 2014.
  12. [12] D. G. Lowe, “Object recognition from local scale-invariant features,” The Proc. of the seventh IEEE Int. Conf. on Computer Vision 1999, Vol.2, 1999.
  13. [13] H. Bay, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features,” European Conf. on computer vision, Springer Berlin Heidelberg, 2006.
  14. [14] F. Endres and N. Engelhard, “An evaluation of the RGB-D SLAM system,” 2012 IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
  15. [15] S. A. Scherer, A. Kloss, and A. Zell, “Loop closure detection using depth images,” 2013 European Conf. on Mobile Robots (ECMR), 2013.
  16. [16] F. Tombari, S. Salti, and L. D. Stefano, “Unique signatures of histograms for local surface description,” European Conf. on computer vision, Springer Berlin Heidelberg, 2010.
  17. [17] G. T. Flitton, T. P. Breckon, and N. M. Bouallagu, “Object Recognition using 3D SIFT in Complex CT Volumes,” British Machine Vision Conference (BMVC), 2010.
  18. [18] I. Sipiran and B. Bustos, “Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes,” The Visual Computer, Vol.27, No.11, pp. 963-976, 2011.
  19. [19] B. Steder, R. B. Rusu, K. Konolige, and W. Burgard, “NARF: 3D range image features for object recognition,” Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Vol.44, 2010.
  20. [20] E. Rosten and T. Drummond, “Machine learning for high speed corner detection” 9th European Conf. on Computer Vision, Vol.1, pp. 430-443, 2006.
  21. [21] E. Rosten, R. Porter, and T. Drummond, “Faster and better: a machine learning approach to corner detection” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.32, pp. 105-119, 2010.
  22. [22] R. B. Rusu, Z. C. Marton, N. Blodow, and M. Beetz, “Persistent point feature histograms for 3D point clouds,” Proc. 10th Int. Conf. Intel. Autonomous Syst. (IAS-10), Baden-Baden, Germany, 2008.
  23. [23] R. B. Rusu, G. Bradski, R. Thibaux, and J. Hsu, “Fast 3d recognition and pose using the viewpoint feature histogram,” 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2010.
  24. [24] R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (FPFH) for 3D registration,” IEEE Int. Conf. on Robotics and Automation (ICRA’09), 2009.
  25. [25] D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” Proc. of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007.
  26. [26] D. W. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” J. of the society for Industrial and Applied Mathematics, Vol.11, No.2, pp. 431-441, 1963.
  27. [27] J. J. Moré, “The Levenberg-Marquardt algorithm: implementation and theory,” Numerical analysis, Springer Berlin Heidelberg, pp. 105-116, 1978.
  28. [28] R. Kummerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, “g2o: A general framework for graph optimization,” 2011 IEEE Int. Conf. on Robotics and Automation (ICRA), 2011.
  29. [29] D. M. W. Powers, “Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness and Correlation,” Int. J. of Machine Learning Technology, Vol.2, No.1, pp. 37-63, 2011.
  30. [30] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, “KinectFusion: Real-time dense surface mapping and tracking,” 2011 10th IEEE Int. symposium on Mixed and augmented reality (ISMAR), 2011.

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Last updated on Sep. 20, 2017