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Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment

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Robot Intelligence Technology and Applications 3

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 345))

Abstract

This paper proposes a robust visual odometry algorithm using a Kinect-style RGB-D sensor and inertial measurement unit (IMU) in a highly dynamic environment. Based on SURF (Speed Up Robust Features) descriptor, the proposed algorithm generates 3-D feature points incorporating depth information into RGB color information. By using an IMU, the generated 3-D feature points are rotated in order to have the same rigid body rotation component between two consecutive images. Before calculating the rigid body transformation matrix between the successive images from the RGB-D sensor, the generated 3-D feature points are filtered into dynamic or static feature points using motion vectors. Using the static feature points, the rigid body transformation matrix is finally computed by RANSAC (RANdom SAmple Consensus) algorithm. The experiments demonstrate that visual odometry is successfully obtained for a subject and a mobile robot by the proposed algorithm in a highly dynamic environment. The comparative study between proposed method and conventional visual odometry algorithm clearly show the reliability of the approach for computing visual odometry in a highly dynamic environment.

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References

  1. Mourikis, A.I., Trawny, N., Roumeliotis, S.I., Johnson, A.E., Matthies, L.: Vision-aided inertial navigation for precise planetary landing: Analysis and experiments. In: Robotics: Science and Systems (2007)

    Google Scholar 

  2. Jeong, W., Lee, K.M.: Cv-slam: A new ceiling vision-based slam technique. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), pp. 3195–3200. IEEE (2005)

    Google Scholar 

  3. Baglietto, M., Sgorbissa, A., Verda, D., Zaccaria, R.: Human navigation and mapping with a 6dof imu and a laser scanner. Robotics and Autonomous Systems 59(12), 1060–1069 (2011)

    Article  Google Scholar 

  4. Yoo, J.K., Kim, J.H.: Fuzzy integral-based gaze control architecture incorporated with modified-univector field-based navigation for humanoid robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(1), 125–139 (2012)

    Article  MathSciNet  Google Scholar 

  5. Grzonka, S., Grisetti, G., Burgard, W.: Towards a navigation system for autonomous indoor flying. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 2878–2883. IEEE (2009)

    Google Scholar 

  6. Bachrach, A., Huang, A.S., Maturana, D., Henry, P., Krainin, M., Fox, D., Roy, N.: Visual navigation for micro air vehicles (2011)

    Google Scholar 

  7. Huang, A.S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., Roy, N.: Visual odometry and mapping for autonomous flight using an rgb-d camera. In: Int. Symposium on Robotics Research (ISRR), Flagstaff, Arizona, USA (2011)

    Google Scholar 

  8. Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1052–1067 (2007)

    Article  Google Scholar 

  9. Kneip, L., Chli, M., Siegwart, R.: Robust real-time visual odometry with a single camera and an imu. In: BMVC, pp. 1–11 (2011)

    Google Scholar 

  10. Han, S., Kim, J., Myung, H., et al.: Landmark-based particle localization algorithm for mobile robots with a fish-eye vision system. IEEE/ASME Transactions on Mechatronics PP(99), 1–12 (2012)

    Google Scholar 

  11. Konolige, K., Agrawal, M., Solà, J.: Large-scale visual odometry for rough terrain. In: Kaneko, M., Nakamura, Y. (eds.) Robotics Research. STAR, vol. 66, pp. 201–212. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. Hu, G., Huang, S., Zhao, L., Alempijevic, A., Dissanayake, G.: A robust rgb-d slam algorithm. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1714–1719. IEEE (2012)

    Google Scholar 

  13. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the rgb-d slam system. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1691–1696. IEEE (2012)

    Google Scholar 

  14. Yang, C., Medioni, G.: Object modelling by registration of multiple range images. Image and Vision Computing 10(3), 145–155 (1992)

    Article  Google Scholar 

  15. Challis, J.H.: A procedure for determining rigid body transformation parameters. Journal of Biomechanics 28(6), 733–737 (1995)

    Article  Google Scholar 

  16. Kim, D.-H., Kim, J.-H.: Image-based ICP algorithm for visual odometry using a RGB-D sensor in a dynamic environment. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications. AISC, vol. 208, pp. 423–430. Springer, Heidelberg (2013)

    Google Scholar 

  17. Nistér, D.: Preemptive ransac for live structure and motion estimation. Machine Vision and Applications 16(5), 321–329 (2005)

    Article  Google Scholar 

  18. Steinbrucker, F., Sturm, J., Cremers, D.: Real-time visual odometry from dense rgb-d images. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 719–722. IEEE (2011)

    Google Scholar 

  19. Tardif, J.P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidirectional camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 2531–2538. IEEE (2008)

    Google Scholar 

  20. Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, p. I–652. IEEE (2004)

    Google Scholar 

  21. Konolige, K., Agrawal, M., Bolles, R.C., Cowan, C., Fischler, M., Gerkey, B.: Outdoor mapping and navigation using stereo vision. In: Khatib, O., Kumar, V., Rus, D. (eds.) Experimental Robotics. STAR, vol. 39, pp. 179–190. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: Kinectfusion: Real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136. IEEE (2011)

    Google Scholar 

  23. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments. The International Journal of Robotics Research 31(5), 647–663 (2012)

    Article  Google Scholar 

  24. Kaess, M., Ranganathan, A., Dellaert, F.: isam: Incremental smoothing and mapping. IEEE Transactions on Robotics 24(6), 1365–1378 (2008)

    Article  Google Scholar 

  25. Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, ICRA (2011)

    Google Scholar 

  26. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J., Dellaert, F.: isam2: Incremental smoothing and mapping with fluid relinearization and incremental variable reordering. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3281–3288. IEEE (2011)

    Google Scholar 

  27. Ho, K.L., Newman, P.: Detecting loop closure with scene sequences. International Journal of Computer Vision 74(3), 261–286 (2007)

    Article  Google Scholar 

  28. Kim, D.H., Kim, J.H.: Visual loop-closure detection method using average feature descriptors. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications 2. AISC, vol. 274, pp. 113–118. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea

    Deok-Hwa Kim, Seung-Beom Han & Jong-Hwan Kim

Authors
  1. Deok-Hwa Kim
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  2. Seung-Beom Han
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  3. Jong-Hwan Kim
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Corresponding author

Correspondence to Deok-Hwa Kim .

Editor information

Editors and Affiliations

  1. Div. Electrical Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Korea, Republic of (South Korea)

    Jong-Hwan Kim

  2. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China

    Weimin Yang

  3. School of Information and Communication Technology, Griffith University, Gold Coast, Australia

    Jun Jo

  4. Center for Intelligent Technologies, Technical University of Kosice, Kosice, Slovakia

    Peter Sincak

  5. Civil and Environmental Engg., KAIST, Daejeon, Korea, Republic of (South Korea)

    Hyun Myung

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© 2015 Springer International Publishing Switzerland

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Kim, DH., Han, SB., Kim, JH. (2015). Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-16841-8_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16840-1

  • Online ISBN: 978-3-319-16841-8

  • eBook Packages: EngineeringEngineering (R0)

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