FastSLAM A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics /

This monograph describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). SLAM addresses the problem of acquiring an environment map with a roving robot, while simultaneously localizing the robot relative to this map. This problem has received enor...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Montemerlo, Michael (Συγγραφέας), Thrun, Sebastian (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
Σειρά:Springer Tracts in Advanced Robotics, 27
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 1 Introduction
  • Applications of SLAM, Joint Estimation, Posterior Estimation, The Extended Kalman Filter, Structure and Sparsity in SLAM, FastSLAM, Outline
  • 2 The SLAM Problem
  • Problem Definition, SLAM Posterior, SLAM as a Markov Chain, Extended Kalman Filtering, Scaling SLAM Algorithms, Robust Data Association, Comparison of FastSLAM to Existing Techniques
  • 3 FastSLAM 1.0
  • Particle Filtering, Factored Posterior Representation, The FastSLAM 1.0 Algorithm, FastSLAM with Unknown Data Association, Summary of the FastSLAM Algorithm, FastSLAM Extensions, Log(N) FastSLAM, Experimental Results, Summary
  • 4 FastSLAM 2.0
  • Sample Impoverishment, FastSLAM 2.0, FastSLAM 2.0 Convergence, Experimental Results, Grid-based FastSLAM, Summary
  • 5 Dynamic Environments
  • SLAM With Dynamic Landmarks, Simultaneous Localization and People Tracking, FastSLAP Implementation,Experimental Results, Summary
  • 6 Conclusions
  • Conclusions, Future Work
  • References, Index.