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...
| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2007, 2007
|
| Edition: | 1st ed. 2007 |
| Series: | Springer Tracts in Advanced Robotics
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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