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

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

By: Michael Montemerlo, Sebastian Thrun

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Hardcover | 1 March 2007

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Hardcover
138 Pages

Dimensions(cm)
23.5 x 15.88 x 1.27

Hardcover

$169.00

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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 Outline2 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 Techniques3 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 Summary4 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 Summary5 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 Summary6 Conclusions 6.1 Conclusions 6.2 Future Work References Index

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Introduction	p. 1
Applications of SLAM	p. 1
Joint Estimation	p. 2
Posterior Estimation	p. 3
The Extended Kalman Filter	p. 5
Quadratic Complexity	p. 5
Single-Hypothesis Data Association	p. 6
Structure and Sparsity in SLAM	p. 7
FastSLAM	p. 8
Logarithmic Complexity	p. 10
Multi-hypothesis Data Association	p. 10
Outline	p. 11
The SLAM Problem	p. 13
Problem Definition	p. 13
SLAM Posterior	p. 15
SLAM as a Markov Chain	p. 16
Bayes Filter Derivation	p. 17
Extended Kalman Filtering	p. 18
Scaling SLAM Algorithms	p. 20
Submap Methods	p. 20
Sparse Extended Information Filters	p. 21
Thin Junction Trees	p. 22
Covariance Intersection	p. 22
Graphical Optimization Methods	p. 22
Robust Data Association	p. 23
Local Map Sequencing	p. 24
Joint Compatibility Branch and Bound	p. 24
Combined Constraint Data Association	p. 25
Iterative Closest Point	p. 25
Multiple Hypothesis Tracking	p. 25
Comparison of FastSLAM to Existing Techniques	p. 26
FastSLAM 1.0	p. 27
Particle Filtering	p. 27
Factored Posterior Representation	p. 29
Proof of the FastSLAM Factorization	p. 30
The FastSLAM 1.0 Algorithm	p. 32
Sampling a New Pose	p. 33
Updating the Landmark Estimates	p. 35
Calculating Importance Weights	p. 37
Importance Resampling	p. 38
Robot Path Posterior Revisited	p. 39
FastSLAM with Unknown Data Association	p. 39
Data Association Uncertainty	p. 39
Per-Particle Data Association	p. 41
Adding New Landmarks	p. 43
Summary of the FastSLAM Algorithm	p. 44
FastSLAM Extensions	p. 46
Greedy Mutual Exclusion	p. 46
Feature Elimination Using Negative Evidence	p. 47
Log(N) FastSLAM	p. 48
Garbage Collection	p. 50
Unknown Data Association	p. 51
Experimental Results	p. 51
Victoria Park	p. 52
Comparison of FastSLAM and the EKF	p. 56
Ambiguous Data Association	p. 59
Sample Impoverishment	p. 59
Summary	p. 62
FastSLAM 2.0	p. 63
Sample Impoverishment	p. 63
FastSLAM 2.0	p. 65
The New Proposal Distribution	p. 66
Calculating the Importance Weights	p. 69
FastSLAM 2.0 Overview	p. 71
Handling Simultaneous Observations	p. 71
FastSLAM 2.0 Convergence	p. 74
Convergence Proof	p. 75
Experimental Results	p. 79
FastSLAM 1.0 Versus FastSLAM 2.0	p. 79
One Particle FastSLAM 2.0	p. 81
Scaling Performance	p. 83
Loop Closing	p. 83
Convergence Speed	p. 85
Grid-Based FastSLAM	p. 87
Summary	p. 90
Dynamic Environments	p. 91
SLAM with Dynamic Landmarks	p. 92
Derivation of the Bayes Filter with Dynamic Objects	p. 93
Factoring the Dynamic SLAM Problem	p. 95
Simultaneous Localization and People Tracking	p. 96
Comparison with Prior Work	p. 97
FastSLAP Implementation	p. 97
Scan-Based Data Association	p. 98
Measurement Model	p. 100
Motion Model	p. 101
Model Selection	p. 101
Experimental Results	p. 102
Tracking and Model Selection Accuracy	p. 102
Global Uncertainty	p. 103
Intelligent Following Behavior	p. 103
Summary	p. 105
Conclusions	p. 107
Conclusion	p. 107
Limitations of FastSLAM	p. 108
Future Work	p. 109
References	p. 111
Index	p. 117
Table of Contents provided by Ingram. All Rights Reserved.

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

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

At a Glance

Hardcover

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