Efficient RGB–D data processing for feature–based self–localization of mobile robots
References
- 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,(11): 1320–1343.
- Bączyk, R. and Kasiński, A. (2010). Visual simultaneous localisation and map-building supported by structured landmarks,(2): 281–293, DOI: 10.2478/v10006-010-0021-7.
- Bailey, T. and Durrant-Whyte, H. (2006). Simultaneous localization and mapping: Part II,(3): 108–117.
- Baker, S. and Matthews, I. (2004). Lucas–Kanade 20 years on: A unifying framework,(3): 221–255.
- Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L. (2008). Speeded-up robust features (SURF),(3): 346–359.
- Belter, D., Nowicki, M. and Skrzypczyński, P. (2015). On the performance of pose-based RGB-D visual navigation systems,D. Cremers(Eds.),, Lecture Notes in Computer Science, Vol. 9004, Springer, Zurich, pp. 1–17.
- Bouguet, J.Y. (2000). Pyramidal implementation of the Lucas–Kanade feature tracker, description of the algorithm,, Intel Corp., Microprocessor Research Labs., Pittsburgh, PA.
- Choi, S., Kim, T. and Yu, W. (2009). Performance evaluation of RANSAC family,.
- Cummins, M. and Newman, P. (2010). Accelerating FAB-MAP with concentration inequalities,(6): 1042–1050.
- Davison, A.J., Reid, I.D., Molton, N.D. and Stasse, O. (2007). MonoSLAM: Real-time single camera SLAM,(6): 1052–1067.
- Durrant-Whyte, H. and Bailey, T. (2006). Simultaneous localization and mapping: Part I,(2): 99–110.
- Eggert, D.W., Lorusso, A. and Fisher, R.B. (1997). Estimating 3-D rigid body transformations: A comparison of four major algorithms,(5–6): 272–290.
- Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D. and Burgard, W. (2012). An evaluation of the RGB-D SLAM system,, pp. 1691–1696.
- Endres, F., Hess, J., Sturm, J., Cremers, D. and Burgard, W. (2014). 3-D mapping with an RGB-D camera,(1): 177–187.
- Engel, J., Sturm, J. and Cremers, D. (2012). Camera-based navigation of a low-cost quadrocopter,, pp. 2815–2821.
- Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise,, pp. 226–231.
- Hansard, M., Lee, S., Choi, O. and Horaud, R. (2012)., Springer, Berlin.
- Hartley, R.I. and Zisserman, A. (2004)., 2nd Edn., Cambridge University Press, Cambridge.
- 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,, pp. 559–568.
- Kerl, C., Sturm, J. and Cremers, D. (2013). Robust odometry estimation for RGB-D cameras,, pp. 3748–3754.
- Khoskelham, K. and Elberink, S.O. (2012). Accuracy and resolution of Kinect depth data for indoor mapping applications,(2): 1437–1454.
- Kraft, M., Nowicki, M., Schmidt, A. and Skrzypczyński, P. (2014). Efficient RGB-D data processing for point-feature-based self-localization,C. Zieliński and K. Tchoń (Eds.),, PW, Warsaw, pp. 245–256, (in Polish).
- Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K. and Burgard, W. (2011). g2o: A general framework for graph optimization,, pp. 3607–3613.
- Lowe, D. (2004). Distinctive image features from scale-invariant keypoints,(2): 91–110.
- Mertens, L., Penne, R. and Ribbens, B. (2013). Time of flight cameras (3D vision),J. Buytaert (Ed.),, Engineering Tools, Techniques and Tables, Nova Science, Hauppauge, NY, pp. 353–417.
- 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,, pp. 1720–1726.
- Nowicki, M. and Skrzypczyński, P. (2013a). Combining photometric and depth data for lightweight and robust visual odometry,, pp. 125–130.
- Nowicki, M. and Skrzypczyński, P. (2013b). Experimental verification of a walking robot self-localization system with the Kinect sensor,(4): 42–51.
- Nüchter, A., Lingemann, K., Hertzberg, J. and Surmann, H. (2007). 6D SLAM—3D mapping outdoor environments,(8–9): 699–722.
- Penne, R., Mertens, L. and Ribbens, B. (2013). Planar segmentation by time-of-flight cameras,J. Blanc-Talon(Eds.),, Lecture Notes in Computer Science, Vol. 8192, Springer, Berlin, pp. 286–297.
- Penne, R., Raposo, C., Mertens, L., Ribbens, B. and Araujo, H. (2015). Investigating new calibration methods without feature detection for ToF cameras,: 50–62.
- Raguram, R., Chum, O., Pollefeys, M., Matas, J. and Frahm, J. (2013). USAC: A universal framework for random sample consenus,(8): 2022–2038.
- Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection,, pp. 430–443.
- Rublee, E., Rabaud, V., Konolige, K. and Bradski, G. (2011). ORB: an efficient alternative to SIFT or SURF,, pp. 2564–2571.
- Rusu, R., Blodow, N., Marton, Z. and Beetz, M. (2008). Aligning point cloud views using persistent feature histograms,, pp. 3384–3391.
- Scaramuzza, D. and Fraundorfer, F. (2011). Visual odometry, Part I: The first 30 years and fundamentals,(4): 80–92.
- Schmidt, A., Fularz, M., Kraft, M., Kasiński, A. and Nowicki, M. (2013a). An indoor RGB-D dataset for the evaluation of robot navigation algorithms,J. Blanc-Talon(Eds.),, Lecture Notes in Computer Science, Vol. 8192, Springer, Berlin, pp. 321–329.
- Schmidt, A., Kraft, M., Fularz, M. and Domagala, Z. (2013b). The comparison of point feature detectors and descriptors in the context of robot navigation,(1): 11–20.
- Segal, A., Haehnel, D. and Thrun, S. (2009). Generalized-ICP,.
- Shi, J. and Tomasi, C. (1994). Good features to track,, pp. 593–600.
- Skrzypczyński, P. (2009). Simultaneous localization and mapping: A feature-based probabilistic approach,(4): 575–588, DOI: 10.2478/v10006-009-0045-z.
- Steder, B., Rusu, R.B., Konolige, K. and Burgard, W. (2011). Point feature extraction on 3D range scans taking into account object boundaries,, pp. 2601–2608.
- Steinbrücker, F., Sturm, J. and Cremers, D. (2011). Real-time visual odometry from dense RGB-D images,, pp. 719–722.
- Stewénius, H., Engels, C. and Nistér, D. (2006). Recent developments on direct relative orientation,(4): 284–294.
- Stoyanov, T., Louloudi, A., Andreasson, H. and Lilienthal, A. (2011). Comparative evaluation of range sensor accuracy in indoor environments,, pp. 19–24.
- Strasdat, H. (2012)., Ph.D. thesis, Imperial College, London.
- Sturm, J., Engelhard, M., Endres, F., Burgard, W. and Cremers, D. (2012). A benchmark for the evaluation of RGB-D SLAM systems,, pp. 573–580.
- Umeyama, S. (1991). Least-squares estimation of transformation parameters between two point patterns,(4): 376–380.
- Whelan, T., McDonald, J., Kaess, M., Fallon, M., Johannsson, H. and Leonard, J. (2012). Kintinuous: Spatially extended KinectFusion,.
- Whelan, T., Johannsson, H., Kaess, M., Leonard, J. and McDonald, J. (2013). Robust real-time visual odometry for dense RGB-D mapping,, pp. 5724–5731.
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
Related subjects:
© 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.