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Random Finite Sets for Robot Mapping & SLAM

New Concepts in Autonomous Robotic Map Representations

  • Book
  • © 2011

Overview

Authors:
  1. John Mullane

    1. School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore

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  2. Ba-Ngu Vo

    1. School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia

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  3. Martin Adams

    1. Department of Electrical Engineering, Universidad de Chile, Santiago, Chile

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  4. Ba-Tuong Vo

    1. School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia

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  • First book on the use of random finite sets (RFS) and finite set statistics (FISST) in robotics
  • Focuses on a critical area of autonomous robotic research - the representation of the environment and its uncertainty
  • Written by leading experts in the field

Part of the book series: Springer Tracts in Advanced Robotics (STAR, volume 72)

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Table of contents (7 chapters)

  1. Front Matter

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  2. Introduction

    1. Introduction

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 1-8

  3. Part I: Random Finite Sets

    1. Front Matter

      Pages 9-9
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    2. Why Random Finite Sets?

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 11-25

    3. Estimation with Random Finite Sets

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 27-42

  4. Part II: Random Finite Set Based Robotic Mapping

    1. Front Matter

      Pages 43-43
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    2. An RFS Theoretic for Bayesian Feature-Based Robotic Mapping

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 45-76

  5. Part III: Random Finite Set Based Simultaneous Localisation and Map Building

    1. Front Matter

      Pages 77-77
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    2. An RFS ‘Brute Force’ Formulation for Bayesian SLAM

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 79-96

    3. Rao-Blackwellised RFS Bayesian SLAM

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 97-126

    4. Extensions with RFSs in SLAM

      • John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo
      Pages 127-136

  6. Back Matter

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Keywords

About this book

The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.

Authors and Affiliations

  • School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    John Mullane

  • School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia

    Ba-Ngu Vo, Ba-Tuong Vo

  • Department of Electrical Engineering, Universidad de Chile, Santiago, Chile

    Martin Adams

Bibliographic Information

  • Book Title: Random Finite Sets for Robot Mapping & SLAM

  • Book Subtitle: New Concepts in Autonomous Robotic Map Representations

  • Authors: John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo

  • Series Title: Springer Tracts in Advanced Robotics

  • DOI: https://doi.org/10.1007/978-3-642-21390-8

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Berlin Heidelberg 2011

  • Hardcover ISBN: 978-3-642-21389-2Published: 19 May 2011

  • Softcover ISBN: 978-3-642-26831-1Published: 15 July 2013

  • eBook ISBN: 978-3-642-21390-8Published: 19 May 2011

  • Series ISSN: 1610-7438

  • Series E-ISSN: 1610-742X

  • Edition Number: 1

  • Number of Pages: XXIV, 148

  • Topics: Robotics and Automation, Artificial Intelligence

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