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2009 | 19 | 4 | 575-588
Tytuł artykułu

Simultaneous Localization And Mapping: A feature-based probabilistic approach

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.
Rocznik
Tom
19
Numer
4
Strony
575-588
Opis fizyczny
Daty
wydano
2009
otrzymano
2009-07-16
Twórcy
  • Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland
Bibliografia
  • Andrade-Cetto, J. and Sanfeliu, A. (2005). The effects of partial observability when building fully correlated maps, IEEE Transactions on Robotics 21(4): 771-777.
  • Andrade-Cetto, J., Vidal-Calleja, T. and Sanfeliu, A. (2005). Unscented transformation of vehicle states in slam, Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 323-328.
  • Arras, K. O., Tomatis, N., Jensen, B. T. and Siegwart, R. (2001). Multisensor on-the-fly localization: Precision and reliability for applications, Robotics and Autonomous Systems 34(2-3): 131-143.
  • Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing 50(2): 174-187.
  • Austin, D. and Jensfelt, P. (2000). Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization, Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, pp. 1036-1041.
  • Bailey, T. (2002). Mobile Robot Localisation and Mapping in Extensive Outdoor Environments, Ph.D. thesis, University of Sydney, Sydney.
  • Bailey, T., Nieto, J., Guivant, J., Stevens, M. and Nebot, E. (2006). Consistency of the EKF-SLAM algorithm, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, Beijing, China, pp. 3562-3567.
  • Bailey, T., Nieto, J. and Nebot, E. (2006). Consistency of the FastSLAM algorithm, Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA, pp. 424-429.
  • Bar-Shalom, Y., Li, X. R. and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation, Wiley, New York, NY.
  • Bosse, M., Newman, P., Leonard, J., Soika, M., Feiten, W. and Teller, S. (2003). An atlas framework for scalable mapping, Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1899-1906.
  • Castellanos, J. A., Neira, J. and Tardós, J. D. (2004). Limits to the consistency of the EKF-based SLAM, Preprints of the IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal, (on CD-ROM).
  • Castellanos, J. A. and Tardós, J. D. (1999). Mobile Robot Localization and Map Building. A Multisensor Fusion Approach, Kluwer, Boston, MA.
  • Castellanos, J. A., Tardós, J. D. and Schmidt, G. (1997). Building a global map of the environment of a mobile robot: The importance of correlations, Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, NM, USA, pp. 1053-1059.
  • 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 Intelligence 29(6): 1052-1067.
  • DiMarco, M., Garulli, A., Giannitrapani, A. and Vicino, A. (2004). A set-theoretic approach to dynamic robot localization and mapping, Autonomous Robots 16(1): 23-47.
  • Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. F. and Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem, IEEE Transactions on Robotics and Automation 17(3): 229-241.
  • Estrada, C., Neira, J. and Tardós, J. D. (2005). Hierarchical SLAM: Real-time accurate mapping of large environments, IEEE Transactions on Robotics 21(4): 588-596.
  • Fox, D., Thrun, S., Burgard, W. and Dellaert, F. (2001). Particle filters for mobile robot localization, in A. Doucet (Ed.), Sequential Monte Carlo Methods in Practice, Springer, Berlin, pp. 499-516.
  • Gasós, J. and Rosetti, A. (1999). Uncertainty representation for mobile robots: Perception, modeling and navigation in unknown environments, Fuzzy Sets and Systems 107(1): 1-24.
  • Guivant, J. and Nebot, E. (2001). Optimization of the simultaneous localization and map-building algorithm for real-time implementation, IEEE Transactions on Robotics and Automation 17(3): 242-257.
  • Huang, S. and Dissanayake, G. (2006). Convergence analysis for extended Kalman filter based slam, Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA, pp. 412-417.
  • Julier, S. J. and Uhlmann, J. K. (2001). A counter example to the theory of simultaneous localization and map building, Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, South Korea, pp. 4238-4243.
  • Kozłowski, K. and Pazderski, D. (2004). Modelling and control of a 4-wheel skid-steering mobile robot, International Journal of Applied Mathematics and Computer Science 14(4): 477-496.
  • Leonard, J. J. and Feder, H. J. S. (2000). A computationally efficient method for large-scale concurrent mapping and localization, Robotics Research: The Ninth International Symposium, Springer, London, pp. 169-179.
  • Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints, International Journal of Computer Vision 60(2): 91-110.
  • Maybeck, P. S. (1979). Stochastic Models, Estimation, and Control, Academic Press, New York, NY.
  • Montemerlo, M. (2003). FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem with Unknown Data Association, Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA.
  • Neira, J. and Tardós, J. D. (2001). Data association in stochastic mapping using the joint compatibility test, IEEE Transactions on Robotics and Automation 17(6): 890-897.
  • Newman, P. and Ho, K. (2005). SLAM-loop closing with visually salient features, Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 644-651.
  • Ortin, D., Neira, J. and Montiel, J. M. M. (2004). Relocation using laser and vision, Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, pp. 1505-1510.
  • Paz, L., Piniés, P., Tardós, J. D. and Neira, J. (2008). Largescale 6-dof SLAM with stereo in hand, IEEE Transactions on Robotics 24(5): 946-957.
  • Skrzypczyński, P. (2005). Uncertainty models of the vision sensors in mobile robot positioning, International Journal of Applied Mathematics and Computer Science 15(1): 73-88.
  • Skrzypczyński, P. (2006). Uncertain spatial knowledge management in a mobile robot architecture, Proceedings of the IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, Heidelberg, Germany, pp. 420-425.
  • Skrzypczyński, P. (2007a). Perception Uncertainty Management in a Mobile Robot Navigation System, Dissertations, No. 407, Poznań University of Technology Press, (in Polish).
  • Skrzypczyński, P. (2007b). Spatial uncertainty management for simultaneous localization and mapping, Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy, pp. 4050-4055.
  • Skrzypczyński, P. (2008). Fusing laser and vision data for perceptually rich environment description, Maintenance Problems (3): 57-67.
  • Smith, R., Self, M. and Cheeseman, P. (1990). Estimating uncertain spatial relationships in robotics, in G. W. I. Cox (Ed.), Autonomous Robot Vehicles, Springer, Berlin, pp. 167-193.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv19i4p575bwm
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