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Efficient RGB–D data processing for feature–based self–localization of mobile robots Cover

Efficient RGB–D data processing for feature–based self–localization of mobile robots

International Journal of Applied Mathematics and Computer Science
Volume 26 (2016): Issue 1 (March 2016)
By: Marek Kraft,  Michał Nowicki,  Rudi Penne,  Adam Schmidt and  Piotr Skrzypczyński  
Open Access
|Mar 2016

References

  1. Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A.S., Krainin, M., Maturana, D., Fox, D. and Roy, N. (2012). Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments, International Journal of Robotics Research31(11): 1320–1343.10.1177/0278364912455256
    Search in Google Scholar
  2. Bączyk, R. and Kasiński, A. (2010). Visual simultaneous localisation and map-building supported by structured landmarks, International Journal of Applied Mathematics and Computer Science20(2): 281–293, DOI: 10.2478/v10006-010-0021-7.10.2478/v10006-010-0021-7
    Search in Google Scholar
  3. Bailey, T. and Durrant-Whyte, H. (2006). Simultaneous localization and mapping: Part II, IEEE Robotics & Automation Magazine13(3): 108–117.10.1109/MRA.2006.1678144
    Search in Google Scholar
  4. Baker, S. and Matthews, I. (2004). Lucas–Kanade 20 years on: A unifying framework, International Journal of Computer Vision56(3): 221–255.10.1023/B:VISI.0000011205.11775.fd
    Search in Google Scholar
  5. Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L. (2008). Speeded-up robust features (SURF), Computer Vision and Image Understanding110(3): 346–359.10.1016/j.cviu.2007.09.014
    Search in Google Scholar
  6. Belter, D., Nowicki, M. and Skrzypczyński, P. (2015). On the performance of pose-based RGB-D visual navigation systems, in D. Cremers et al. (Eds.), Computer Vision, ACCV 2014, Part II, Lecture Notes in Computer Science, Vol. 9004, Springer, Zurich, pp. 1–17.10.1007/978-3-319-16808-1_28
    Search in Google Scholar
  7. Bouguet, J.Y. (2000). Pyramidal implementation of the Lucas–Kanade feature tracker, description of the algorithm, Technical report, Intel Corp., Microprocessor Research Labs., Pittsburgh, PA.
    Search in Google Scholar
  8. Choi, S., Kim, T. and Yu, W. (2009). Performance evaluation of RANSAC family, British Machine Vision Conference, London, UK.10.5244/C.23.81
    Search in Google Scholar
  9. Cummins, M. and Newman, P. (2010). Accelerating FAB-MAP with concentration inequalities, IEEE Transactions on Robotics26(6): 1042–1050.10.1109/TRO.2010.2080390
    Search in Google Scholar
  10. Davison, A.J., Reid, I.D., Molton, N.D. and Stasse, O. (2007). MonoSLAM: Real-time single camera SLAM, IEEE Transactions on Pattern Analysis and Machine Intelligence29(6): 1052–1067.10.1109/TPAMI.2007.104917431302
    Search in Google Scholar
  11. Durrant-Whyte, H. and Bailey, T. (2006). Simultaneous localization and mapping: Part I, IEEE Robotics & Automation Magazine13(2): 99–110.10.1109/MRA.2006.1638022
    Search in Google Scholar
  12. Eggert, D.W., Lorusso, A. and Fisher, R.B. (1997). Estimating 3-D rigid body transformations: A comparison of four major algorithms, Machine Vision and Applications9(5–6): 272–290.10.1007/s001380050048
    Search in Google Scholar
  13. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D. and Burgard, W. (2012). An evaluation of the RGB-D SLAM system, IEEE International Conference on Robotics and Automation, St. Paul MN, USA, pp. 1691–1696.
    Search in Google Scholar
  14. Endres, F., Hess, J., Sturm, J., Cremers, D. and Burgard, W. (2014). 3-D mapping with an RGB-D camera, IEEE Transactions on Robotics30(1): 177–187.10.1109/TRO.2013.2279412
    Search in Google Scholar
  15. Engel, J., Sturm, J. and Cremers, D. (2012). Camera-based navigation of a low-cost quadrocopter, IEEE/RSJ International Conference on Intelligent Robots & Systems, Vilamoura, Portugal, pp. 2815–2821.
    Search in Google Scholar
  16. Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, pp. 226–231.
    Search in Google Scholar
  17. Hansard, M., Lee, S., Choi, O. and Horaud, R. (2012). Time-of-Flight Cameras: Principles, Methods and Applications, Springer, Berlin.10.1007/978-1-4471-4658-2
    Search in Google Scholar
  18. Hartley, R.I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, 2nd Edn., Cambridge University Press, Cambridge.
    Search in Google Scholar
  19. Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A. and Fitzgibbon, A. (2011). KinectFusion: Real-time 3D reconstruction and interaction using a moving depth camera, ACM Symposium on User Interface Software and Technology, New York, NY, pp. 559–568.
    Search in Google Scholar
  20. Kerl, C., Sturm, J. and Cremers, D. (2013). Robust odometry estimation for RGB-D cameras, IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, pp. 3748–3754.
    Search in Google Scholar
  21. Khoskelham, K. and Elberink, S.O. (2012). Accuracy and resolution of Kinect depth data for indoor mapping applications, Sensors12(2): 1437–1454.10.3390/s120201437330412022438718
    Search in Google Scholar
  22. Kraft, M., Nowicki, M., Schmidt, A. and Skrzypczyński, P. (2014). Efficient RGB-D data processing for point-feature-based self-localization, in C. Zieliński and K. Tchoń (Eds.), Postępy robotyki, PW, Warsaw, pp. 245–256, (in Polish).
    Search in Google Scholar
  23. Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K. and Burgard, W. (2011). g2o: A general framework for graph optimization, IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 3607–3613.
    Search in Google Scholar
  24. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision60(2): 91–110.10.1023/B:VISI.0000029664.99615.94
    Search in Google Scholar
  25. Mertens, L., Penne, R. and Ribbens, B. (2013). Time of flight cameras (3D vision), in J. Buytaert (Ed.), Recent Advances in Topography, Engineering Tools, Techniques and Tables, Nova Science, Hauppauge, NY, pp. 353–417.
    Search in Google Scholar
  26. Nascimento, E., Oliveira, G., Campos, M.F.M., Vieira, A. and Schwartz, W. (2012). BRAND: A robust appearance and depth descriptor for RGB-D images, IEEE/RSJ International Conference on Intelligent Robots & Systems, Vilamoura, Portugal, pp. 1720–1726.
    Search in Google Scholar
  27. Nowicki, M. and Skrzypczyński, P. (2013a). Combining photometric and depth data for lightweight and robust visual odometry, European Conference on Mobile Robots (ECMR), Barcelona, Spain, pp. 125–130.10.1109/ECMR.2013.6698831
    Search in Google Scholar
  28. Nowicki, M. and Skrzypczyński, P. (2013b). Experimental verification of a walking robot self-localization system with the Kinect sensor, Journal of Automation, Mobile Robotics & Intelligent Systems7(4): 42–51.10.14313/JAMRIS_4-2013/43
    Search in Google Scholar
  29. Nüchter, A., Lingemann, K., Hertzberg, J. and Surmann, H. (2007). 6D SLAM—3D mapping outdoor environments, Journal of Field Robotics24(8–9): 699–722.10.1002/rob.20209
    Search in Google Scholar
  30. Penne, R., Mertens, L. and Ribbens, B. (2013). Planar segmentation by time-of-flight cameras, in J. Blanc-Talon et al. (Eds.), Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, Vol. 8192, Springer, Berlin, pp. 286–297.10.1007/978-3-319-02895-8_26
    Search in Google Scholar
  31. Penne, R., Raposo, C., Mertens, L., Ribbens, B. and Araujo, H. (2015). Investigating new calibration methods without feature detection for ToF cameras, Image and Vision Computing43: 50–62.10.1016/j.imavis.2015.09.001
    Search in Google Scholar
  32. Raguram, R., Chum, O., Pollefeys, M., Matas, J. and Frahm, J. (2013). USAC: A universal framework for random sample consenus, IEEE Transactions on Pattern Analysis and Machine Intelligence35(8): 2022–2038.10.1109/TPAMI.2012.25723787350
    Search in Google Scholar
  33. Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection, 9th European Conference on Computer Vision (ECCV’06), Graz, Austria, pp. 430–443.
    Search in Google Scholar
  34. Rublee, E., Rabaud, V., Konolige, K. and Bradski, G. (2011). ORB: an efficient alternative to SIFT or SURF, IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, pp. 2564–2571.
    Search in Google Scholar
  35. Rusu, R., Blodow, N., Marton, Z. and Beetz, M. (2008). Aligning point cloud views using persistent feature histograms, IEEE/RSJ International Conference on Intelligent Robots & Systems, Nice, France, pp. 3384–3391.
    Search in Google Scholar
  36. Scaramuzza, D. and Fraundorfer, F. (2011). Visual odometry, Part I: The first 30 years and fundamentals, IEEE Robotics & Automation Magazine18(4): 80–92.10.1109/MRA.2011.943233
    Search in Google Scholar
  37. Schmidt, A., Fularz, M., Kraft, M., Kasiński, A. and Nowicki, M. (2013a). An indoor RGB-D dataset for the evaluation of robot navigation algorithms, in J. Blanc-Talon et al. (Eds.), Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, Vol. 8192, Springer, Berlin, pp. 321–329.
    Search in Google Scholar
  38. Schmidt, A., Kraft, M., Fularz, M. and Domagala, Z. (2013b). The comparison of point feature detectors and descriptors in the context of robot navigation, Journal of Automation, Mobile Robotics & Intelligent Systems7(1): 11–20.
    Search in Google Scholar
  39. Segal, A., Haehnel, D. and Thrun, S. (2009). Generalized-ICP, Robotics: Science and Systems, Seattle, WA, USA.10.15607/RSS.2009.V.021
    Search in Google Scholar
  40. Shi, J. and Tomasi, C. (1994). Good features to track, IEEE Conference on Computer Vision and Pattern Recognition (CVPR’94), Seattle, WA, USA, pp. 593–600.
    Search in Google Scholar
  41. Skrzypczyński, P. (2009). Simultaneous localization and mapping: A feature-based probabilistic approach, International Journal of Applied Mathematics and Computer Science19(4): 575–588, DOI: 10.2478/v10006-009-0045-z.10.2478/v10006-009-0045-z
    Search in Google Scholar
  42. Steder, B., Rusu, R.B., Konolige, K. and Burgard, W. (2011). Point feature extraction on 3D range scans taking into account object boundaries, IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 2601–2608.
    Search in Google Scholar
  43. Steinbrücker, F., Sturm, J. and Cremers, D. (2011). Real-time visual odometry from dense RGB-D images, Workshop on Live Dense Reconstruction with Moving Cameras/International Conference on Computer Visision, Barcelona, Spain, pp. 719–722.
    Search in Google Scholar
  44. Stewénius, H., Engels, C. and Nistér, D. (2006). Recent developments on direct relative orientation, ISPRS Journal of Photogrammetry and Remote Sensing60(4): 284–294.10.1016/j.isprsjprs.2006.03.005
    Search in Google Scholar
  45. Stoyanov, T., Louloudi, A., Andreasson, H. and Lilienthal, A. (2011). Comparative evaluation of range sensor accuracy in indoor environments, 5th European Conference on Mobile Robots, Örebro, Sweden, pp. 19–24.
    Search in Google Scholar
  46. Strasdat, H. (2012). Local Accuracy and Global Consistency for Efficient Visual SLAM, Ph.D. thesis, Imperial College, London.
    Search in Google Scholar
  47. Sturm, J., Engelhard, M., Endres, F., Burgard, W. and Cremers, D. (2012). A benchmark for the evaluation of RGB-D SLAM systems, IEEE/RSJ International Conference on Intelligent Robots & Systems, Vilamoura, Portugal, pp. 573–580.
    Search in Google Scholar
  48. Umeyama, S. (1991). Least-squares estimation of transformation parameters between two point patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence13(4): 376–380.10.1109/34.88573
    Search in Google Scholar
  49. Whelan, T., McDonald, J., Kaess, M., Fallon, M., Johannsson, H. and Leonard, J. (2012). Kintinuous: Spatially extended KinectFusion, Robotics: Science and Systems, Sydney, Australia.
    Search in Google Scholar
  50. Whelan, T., Johannsson, H., Kaess, M., Leonard, J. and McDonald, J. (2013). Robust real-time visual odometry for dense RGB-D mapping, IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, pp. 5724–5731.
    Search in Google Scholar
DOI: https://doi.org/10.1515/amcs-2016-0005 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 63 - 79
Submitted on: Sep 28, 2014
|
Published on: Mar 31, 2016
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
visual odometry,
simultaneous localization and mapping,
RGB-D,
tracking,
point features
Related subjects:
Mathematics,
Applied mathematics

© 2016 Marek Kraft, Michał Nowicki, Rudi Penne, Adam Schmidt, Piotr Skrzypczyński, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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