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

Write A Review

Hardcover | 1 March 2007

At a Glance

Hardcover
138 Pages

Dimensions(cm)
23.5 x 15.88 x 1.27

Hardcover

$169.00

or 4 interest-free payments of $42.25 with

or

Ships in 5 to 7 business days

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

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

More in Robotics

LEGO

Gadgets (Klutz)

Novelty Book

RRP $34.99

$25.75

26%
OFF

Metaheuristics in Engineering Applications

Advances in Metaheuristics

Hardcover

RRP $294.00

$254.75

13%
OFF

Scary Smart : The Future of Artificial Intelligence and How You Can Save Our World - Mo Gawdat

$26.75

44%
OFF

Make: More Electronics

Journey Deep into the World of Logic Chips, Amplifiers, Sensors, and Randomicity

Paperback

RRP $66.75

$34.99

48%
OFF

Encyclopedia of Electronic Components Volume 2

Paperback

RRP $57.00

$30.75

46%
OFF

Learning the Art of Electronics

A Hands-On Lab Course

Paperback

RRP $134.95

$112.75

16%
OFF

Artificial Intelligence For Dummies

For Dummies (Computer/Tech)

Paperback

RRP $49.95

$38.75

22%
OFF

Missions to Mars

A New Era of Rover and Spacecraft Discovery on the Red Planet

Hardcover

RRP $55.00

$40.75

26%
OFF

AI 2041

Ten Visions for Our Future

Paperback

RRP $29.99

$24.99

17%
OFF

Assistive Robotics

Modeling, Computation, and Realization

Hardcover

RRP $315.00

$271.99

14%
OFF

Assistive Robotics

Perspectives, Challenges, and Firmware

Hardcover

RRP $294.00

$254.75

13%
OFF

Coexistence and Coevolution of Humans and Intelligent, Adaptive Environments

Paperback

RRP $94.99

$85.75

10%
OFF

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

Shipping

How to return your order

Defective items

You Can Find This Book In

More in Robotics

LEGO

Gadgets (Klutz)

Metaheuristics in Engineering Applications

Advances in Metaheuristics

Scary Smart

The Future of Artificial Intelligence and How You Can Save Our World

Recent Developments in Mechatronics

Design and Applications of Mobile Robotics

Modeling, Planning and Control of Robotics

Kinematic and Dynamic Analysis of Parallel Manipulators

Fundamentals of Robotics

Construction in the Age of Automation and Robotics

Mobile Robotics

Models, Methods and Applications

Automation and Robotics in the Construction Industry

Robotics

Intelligent Systems, Control and Automation

Intelligent Mechatronic Systems

Modeling and Control

Theory and Applications of Parallel Manipulators

Encyclopedia of Electronic Components

Resistors, Capacitors, Inductors, Semiconductors, Electromagnetism

Make: More Electronics

Journey Deep into the World of Logic Chips, Amplifiers, Sensors, and Randomicity

Encyclopedia of Electronic Components Volume 2

Learning the Art of Electronics

A Hands-On Lab Course

Artificial Intelligence For Dummies

For Dummies (Computer/Tech)

Missions to Mars

A New Era of Rover and Spacecraft Discovery on the Red Planet

AI 2041

Ten Visions for Our Future

Assistive Robotics

Modeling, Computation, and Realization

Assistive Robotics

Perspectives, Challenges, and Firmware

Coexistence and Coevolution of Humans and Intelligent, Adaptive Environments

This product is categorised by