FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics / Edition 1

FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics / Edition 1

by Michael Montemerlo, Sebastian Thrun
     
 

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ISBN-10: 3642079784

ISBN-13: 9783642079788

Pub. Date: 11/19/2010

Publisher: Springer Berlin Heidelberg

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

Overview

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.

Product Details

ISBN-13:
9783642079788
Publisher:
Springer Berlin Heidelberg
Publication date:
11/19/2010
Series:
Springer Tracts in Advanced Robotics Series, #27
Edition description:
Softcover reprint of hardcover 1st ed. 2007
Pages:
120
Product dimensions:
6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

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

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