E-Book, Englisch, Band 27, 120 Seiten, eBook
Montemerlo / Thrun FastSLAM
2007
ISBN: 978-3-540-46402-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics
E-Book, Englisch, Band 27, 120 Seiten, eBook
Reihe: Springer Tracts in Advanced Robotics
ISBN: 978-3-540-46402-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
This monograph describes a new family of algorithms for the simultaneous localization and mapping (SLAM) problem in robotics, called FastSLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including a solution to the problem of people tracking.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1 Introduction 1.1 Applications of SLAM 1.2 Joint Estimation 1.3 Posterior Estimation 1.4 The Extended Kalman Filter 1.5 Structure and Sparsity in SLAM 1.6 FastSLAM 1.7 Outline
2 The SLAM Problem 2.1 Problem Definition 2.2 SLAM Posterior 2.3 SLAM as a Markov Chain 2.4 Extended Kalman Filtering 2.5 Scaling SLAM Algorithms 2.6 Robust Data Association 2.7 Comparison of FastSLAM to Existing Techniques
3 FastSLAM 1.0 3.1 Particle Filtering 3.2 Factored Posterior Representation 3.3 The FastSLAM 1.0 Algorithm 3.4 FastSLAM with Unknown Data Association 3.5 Summary of the FastSLAM Algorithm 3.6 FastSLAM Extensions 3.7 Log(N) FastSLAM 3.8 Experimental Results 3.9 Summary
4 FastSLAM 2.0 4.1 Sample Impoverishment 4.2 FastSLAM 2.0 4.3 FastSLAM 2.0 Convergence 4.4 Experimental Results 4.5 Grid-based FastSLAM 4.6 Summary
5 Dynamic Environments 5.1 SLAM With Dynamic Landmarks 5.2 Simultaneous Localization and People Tracking 5.3 FastSLAP Implementation 5.4 Experimental Results 5.5 Summary
6 Conclusions 6.1 Conclusions 6.2 Future Work
References Index
