FastSLAM
A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „FastSLAM “
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
Bibliographische Angaben
- Autoren: Sebastian Thrun , Michael Montemerlo
- 2007, 136 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer Berlin Heidelberg
- ISBN-10: 3540463992
- ISBN-13: 9783540463993
- Erscheinungsdatum: 18.01.2007
Sprache:
Englisch
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