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Indoor Scene Recognition by 3-D Object Search

For Robot Programming by Demonstration

  • Book
  • © 2020

Overview

Authors:
  1. Pascal Meißner

    1. IAR-IPR, Karlsruhe Institute of Technology , Karlsruhe, Germany

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  • Focuses on enabling mobile robots to recognize scenes in distributed and cluttered indoor environments
  • Connects two research problems (scene recognition and 3-D object search), which are usually investigated separately in different research fields
  • Presents numerous complex experiments on a real-world robot

Part of the book series: Springer Tracts in Advanced Robotics (STAR, volume 135)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xix
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  2. Introduction

    • Pascal Meißner
    Pages 1-22

  3. Related Work

    • Pascal Meißner
    Pages 23-42

  4. Passive Scene Recognition

    • Pascal Meißner
    Pages 43-124

  5. Active Scene Recognition

    • Pascal Meißner
    Pages 125-176

  6. Evaluation

    • Pascal Meißner
    Pages 177-248

  7. Summary

    • Pascal Meißner
    Pages 249-258

  8. Back Matter

    Pages 259-262
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Keywords

About this book

This book focuses on enabling mobile robots to recognize scenes in indoor environments, in order to allow them to determine which actions are appropriate at which points in time. In concrete terms, future robots will have to solve the classification problem represented by scene recognition sufficiently well for them to act independently in human-centered environments. To achieve accurate yet versatile indoor scene recognition, the book presents a hierarchical data structure for scenes – the Implicit Shape Model trees. Further, it also provides training and recognition algorithms for these trees. In general, entire indoor scenes cannot be perceived from a single point of view. To address this problem the authors introduce Active Scene Recognition (ASR), a concept that embeds canonical scene recognition in a decision-making system that selects camera views for a mobile robot to drive to so that it can find objects not yet localized. The authors formalize the automatic selection of cameraviews as a Next-Best-View (NBV) problem to which they contribute an algorithmic solution, which focuses on realistic problem modeling while maintaining its computational efficiency. Lastly, the book introduces a method for predicting the poses of objects to be searched, establishing the otherwise missing link between scene recognition and NBV estimation.

Authors and Affiliations

  • IAR-IPR, Karlsruhe Institute of Technology , Karlsruhe, Germany

    Pascal Meißner

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