PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2016 | 26 | 3 | 669-682
Tytuł artykułu

Vision based persistent localization of a humanoid robot for locomotion tasks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Typical monocular localization schemes involve a search for matches between reprojected 3D world points and 2D image features in order to estimate the absolute scale transformation between the camera and the world. Successfully calculating such transformation implies the existence of a good number of 3D points uniformly distributed as reprojected pixels around the image plane. This paper presents a method to control the march of a humanoid robot towards directions that are favorable for visual based localization. To this end, orthogonal diagonalization is performed on the covariance matrices of both sets of 3D world points and their 2D image reprojections. Experiments with the NAO humanoid platform show that our method provides persistence of localization, as the robot tends to walk towards directions that are desirable for successful localization. Additional tests demonstrate how the proposed approach can be incorporated into a control scheme that considers reaching a target position.
Rocznik
Tom
26
Numer
3
Strony
669-682
Opis fizyczny
Daty
wydano
2016
otrzymano
2015-09-18
poprawiono
2016-03-13
zaakceptowano
2016-04-04
Twórcy
  • Robotics and Advanced Manufacturing Group, Research Center for Advanced Studies, National Polytechnic Institute (CINVESTAV), Ramos Arizpe, Coahuila, 25900, Mexico
  • Robotics and Advanced Manufacturing Group, Research Center for Advanced Studies, National Polytechnic Institute (CINVESTAV), Ramos Arizpe, Coahuila, 25900, Mexico
  • Robotics and Advanced Manufacturing Group, Research Center for Advanced Studies, National Polytechnic Institute (CINVESTAV), Ramos Arizpe, Coahuila, 25900, Mexico
Bibliografia
  • Alcantarilla, P.-F., Stasse, O., Druon, S., Bergasa, L.-M. and Dellaert, F. (2013). How to localize humanoids with a single camera?, Autonomous Robots 38(1-2): 47-71.
  • Davison, A., Reid, I.-D., Molton, N.-D. and Stasse, O. (2007). Monoslam: Real-time single camera SLAM, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6): 1052-1067.
  • Delfin, J., Becerra, H.M. and Arechavaleta, G. (2014). Visual path following using a sequence of target images and smooth robot velocities for humanoid navigation, IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain, pp. 354-359.
  • Dellaert, F. and Kaess, M. (2006). Square root SLAM: Simultaneous localization and mapping via square root information smoothing, International Journal of Robotics Research 25(12): 1181-1203.
  • Durrant-Whyte, H. and Bailey, T. (2006). Simultaneous localization and mapping: Part I, IEEE Robotics and Automation Magazine 13(2): 99-110.
  • Endres, F., Hess, J., Sturm, J., Cremers, D. and Burgard, W. (2014). 3-D mapping with an RGB-D camera, IEEE Transactions on Robotics 30(1): 177-187.
  • Gouaillier, D., Collette, C. and Kilner, C. (2010). Omni-directional closed-loop walk for NAO, IEEERAS International Conference on Humanoid Robots, Nashville, TN, USA, pp. 448-454.
  • Hartley, R.I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press, New York, NY.
  • Henry, P., Krainin, M., Herbst, E., Ren, X. and Fox, D. (2012). RGB-D mapping: Using kinect-style depth cameras for dense 3D modeling of indoor environments, International Journal of Robotics Research 31(5): 647-663.
  • Herdt, A., Holger, D., Wieber, P.-B., Dimitrov, D., Mombaur, K. and Moritz, D. (2010). Online walking motion generation with automatic foot step placement, Advanced Robotics 24(5-6): 719-737.
  • Hornung, A., Osswald, S., Maier, D. and Bennewitz, M. (2014). Monte Carlo localization for humanoid robot navigation in complex indoor environments, International Journal of Humanoid Robotics 11(02), Article ID: 1441002.
  • Hornung, A., Wurm, K. and Bennewitz, M. (2010). Humanoid robot localization in complex indoor environments, IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 1690-1695.
  • Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C. and Burgard, W. (2013). OctoMap: An efficient probabilistic 3D mapping framework based on octrees, Autonomous Robots 34(3): 189-206.
  • Ido, J., Shimizu, Y., Matsumoto, Y. and Ogasawara, T. (2009). Indoor navigation for a humanoid robot using a view sequence, International Journal of Robotics Research 28(2): 315-325.
  • Kajita, S., Kanehiro, F., Fujiwara, K., Harada, K., Yokoi, K. and Hirukawa, H. (2003). Biped walking pattern generation by using preview control of zero-moment point, IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1620-1626.
  • Kerl, C., Sturm, J. and Cremers, D. (2013a). Dense visual SLAM for RGB-D cameras, IEEE International Conference on Intelligent Robots and Systems, Tokyo, Japan, pp. 2100-2106.
  • Kerl, C., Sturm, J. and Cremers, D. (2013b). Robust odometry estimation for RGB-D cameras, IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, pp. 3748-3754.
  • Klein, G. and Murray, D. (2007). Parallel tracking and mapping for small AR workspaces, 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'07), Nara, Japan, pp. 225-234.
  • Lourakis, M. A. and Argyros, A. (2009). SBA: A software package for generic sparse bundle adjustment, ACM Transactions on Mathematical Software 1(36): 1-30.
  • Maier, D., Hornung, A. and Bennewitz, M. (2012). Real-time navigation in 3D environments based on depth camera data, IEEE International Conference on Humanoid Robots, Osaka, Japan, pp. 692-697.
  • Martínez, P.A., Varas, D., Castelán, M., Camacho, M., Marques, F. and Arechavaleta, G. (2014). 3D shape reconstruction from a humanoid generated video sequence, IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain, pp. 699-706.
  • Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F. and Sayd, P. (2009). Generic and real-time structure from motion using local bundle adjustment, Image and Vision Computing 8(27): 1178-1193.
  • Obwald, S., Hornung, A. and Bennewitz, M. (2012). Improved proposals for highly accurate localization using range and vision data, IEEE/RSJ International Conference on Intelligent Robots and System, Vilamoura, Portugal, pp. 1809-1814.
  • Oriolo, G., Paolillo, A., Rosa, L. and Vendittelli, M. (2013). Vision-based trajectory control for humanoid navigation, IEEE-RAS International Conference on Humanoid Robots, Atlanta, GA, USA, pp. 113-123.
  • Oriolo, G., Paolillo, A., Rosa, L. and Vendittelli, M. (2016). Humanoid odometric localization integrating kinematic, inertial and visual information, Autonomous Robots 40(5): 867-879.
  • Rosten, E. and Drummond, T. (2005). Fusing points and lines for high performance tracking, 10th IEEE International Conference on Computer Vision, Beijing, China, Vol. 2, pp. 1508-1515.
  • Royer, E., Lhuillier, M., Dhome, M. and Lavest, J.M. (2007). Monocular vision for mobile robot localization and autonomous navigation, International Journal of Computer Vision 74(3): 237-260.
  • Santana, A.M. and Medeiros, A.A.D. (2012). Straight-lines modelling using planar information for monocular SLAM, International Journal of Applied Mathematics and Computer Science 22(2): 409-421, DOI: 10.2478/v10006-012-0031-8.
  • Scaramuzza, D. and Fraundorfer, F. (2011). Visual odometry, IEEE Robotics and Automation Magazine 18(4): 80-92.
  • Segal, A., Haehnel, D. and Thrun, S. (2009). Generalized-ICP, in J. Trinkle et al. (Eds.), Proceedings of Robotics: Science and Systems, The MIT Press, Cambridge, MA.
  • Skrzypczyński, P. (2009). Simultaneous localization and mapping: A feature-based probabilistic approach, International Journal of Applied Mathematics and Computer Science 19(4): 575-588, DOI: 10.2478/v10006-009-0045-z.
  • Stasse, O., Davison, A., Sellaouti, R. and Yokoi, K. (2006). Real-time 3D SLAM for a humanoid robot considering pattern generator information, IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, pp. 348-355.
  • Strasdat, H., Montiel, J. and Davison, A. (2010). Real-time monocular SLAM: Why filter?, IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, pp. 2657-2664.
  • Sturm, J., Engelhard, N., Endres, F., Burgard, W. and Cremers, D. (2012). A benchmark for the evaluation of RGB-D SLAM systems, IEEE International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, pp. 573-580.
  • Triggs, B., McLauchlan, P., Hartley, R. and Fitzgibbon, A. (1999). Bundle adjustment a modern synthesis, in B. Triggs et al. (Eds.), Vision Algorithms: Theory and Practice, Lecture Notes in Computer Science, Vol. 1883, Springer-Verlag, London, pp. 298-372.
  • Wurm, K. M., Hornung, A., Bennewitz, M., Stachniss, C. and Bur-gard, W. (2010). OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems, IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv26i3p669bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.