Active Sensor Planning for Multiview Vision Tasks

  • Shengyong Chen
  • Y. F. Li
  • Jianwei Zhang
  • Wanliang Wang

Table of contents

  1. Front Matter
    Pages I-XI
  2. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 1-10
  3. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 11-38
  4. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 39-66
  5. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 67-80
  6. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 81-100
  7. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 101-118
  8. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 119-146
  9. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 147-176
  10. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 177-206
  11. Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang
    Pages 207-231
  12. Back Matter
    Pages 233-259

About this book

Introduction

Vision sensors have limited fields of views and can only "see" a portion of a scene from a single viewpoint. To make the entire object visible, the sensor has to be moved from one place to another around the object to observe all features of interest, which brings a multiview vision task that has to be solved by means of active perception. The sensor planning presented in this book describes some effective strategies to generate a sequence of viewing poses and sensor settings for optimally completing a perception task. Several methods are proposed to solve the problems in both model-based and nonmodel-based vision tasks. For model-based applications, the method involves determination of the optimal sensor placements and a shortest path through these viewpoints for automatic generation of a perception plan. For nonmodel-based applications, the method involves determination of the best next view and sensor settings, to incrementally acquire the object information and to find geometrical cues to predict the unknown portion of an object or environment.

The ten chapters in Active Vision Planning draw on recent work in robot vision over ten years, particularly in the use of new concepts of active sensing, reconfiguration, recalibration, sensor modeling, sensing constraints, sensing evaluation, viewpoint decision, sensor placement graph, model based planning, path planning, planning for robot in unknown environment, dynamic 3D construction, surface prediction, etc. Implementation examples are also provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.

Keywords

3D 3D active sensing Automatic Sensor placement and 3D sensing Information Sensor Sensor configuration and recalibration Trend algorithms entropy genetic algorithms information entropy robot robot vision sensing topology

Editors and affiliations

  • Shengyong Chen
    • 1
  • Y. F. Li
    • 2
  • Jianwei Zhang
    • 3
  • Wanliang Wang
    • 4
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhou 310014PR China
  2. 2.Dept of Manufacturing Engineering and Engineering Management CityUniversity of Hong KongKowloonHong Kong
  3. 3.AB TAMS Department of InformaticsUniversity of HamburgD-22527 HamburgGermany
  4. 4.College of Software EngineeringZhejiang University of TechnologyHangzhou 310014PR China

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-77072-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-77071-8
  • Online ISBN 978-3-540-77072-5
  • About this book