Chen / Zhang / Wang Active Sensor Planning for Multiview Vision Tasks

1. Auflage 2008
ISBN: 978-3-540-77072-5
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 265 Seiten

An active robot system can change its visual parameters in an intentional manner and perform its sensing actions purposefully. A general vision task thus can be performed in an efficient way by means of strategic control of the perception process. The controllable processes include 3D active sensing, sensor configuration and recalibration, automatic sensor placement, and 3D sensing. This book explores these important issues in studying for active visual perception.

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. The sensor planning presented in this book describes an effective strategy 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. A topology of viewpoints is achieved by a genetic algorithm in which a min-max criterion is used for evaluation. A shortest path is also determined by graph algorithms. For nonmodel-based applications, the method involves determination of the best next view and sensor settings. The trend surface is proposed as the cue to predict the unknown portion of an object or environment.

The 11 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 model, 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.

Autoren/Hrsg.

Chen, Shengyong

Zhang, Jianwei

Wang, Wanliang

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Chapter 1 Introduction;12
3.1;1.1 Motivations;12
3.2;1.2 Objectives and Solutions;18
3.3;1.3 Book Structure;19
4;Chapter 2 Active Vision Sensors;21
4.1;2.1 3D Visual Sensing by Machine Vision;21
4.2;2.2 3D Sensing by Stereo Vision Sensors;29
4.3;2.3 3D Sensing by Stripe Light Vision Sensors;33
4.4;2.4 3D Sensor Reconfiguration and Recalibration;37
4.5;2.5 Summary;48
5;Chapter 3 Active Sensor Planning – the State-of-the-Art;49
5.1;3.1 The Problem;49
5.2;3.2 Overview of the Recent Development;50
5.3;3.3 Fundamentals of Sensor Modeling and Planning;53
5.4;3.4 Planning for Dimensional Inspection;58
5.5;3.5 Planning for Recognition and Search;61
5.6;3.6 Planning for Exploration, Navigation, and Tracking;64
5.7;3.7 Planning for Assembly and Disassembly;69
5.8;3.8 Planning with Illumination;70
5.9;3.9 Other Planning Tasks;73
5.10;3.10 Summary;76
6;Chapter 4 Sensing Constraints and Evaluation;77
6.1;4.1 Representation of Vision Sensors;77
6.2;4.2 Placement Constraints;78
6.3;4.3 Common Approaches to Viewpoint Evaluation;85
6.4;4.4 Criterion of Lowest Operation Cost;87
6.5;4.5 Summary;90
7;Chapter 5 Model-Based Sensor Planning;91
7.1;5.1 Overview of the Method;91
7.2;5.2 Sensor Placement Graph;92
7.3;5.3 The Shortest Path;95
7.4;5.4 Practical Considerations;97
7.5;5.5 Implementation;102
7.6;5.6 Summary;110
8;Chapter 6 Planning for Freeform Surface Measurement;111
8.1;6.1 The Problem;111
8.2;6.2 B-Spline Model Representation;114
8.3;6.3 Uncertainty Analysis;118
8.4;6.4 Sensing Strategy for Optimizing Measurement;120
8.5;6.5 Experiments;122
8.6;6.6 Summary;128
9;Chapter 7 Sensor Planning for Object Modeling;129
9.1;7.1 Planning Approaches to Model Construction;129
9.2;7.2 The Procedure for Model Construction;134
9.3;7.3 Self-Termination Criteria;137
9.4;7.4 Experiments;141
9.5;7.5 Summary;154
10;Chapter 8 Information Entropy Based Planning;156
10.1;8.1 Overview;156
10.2;8.2 Model Representation;157
10.3;8.3 Expected Error;163
10.4;8.4 View Planning;166
10.5;8.5 Experiments;168
10.6;8.6 Summary;184
11;Chapter 9 Model Prediction and Sensor Planning;185
11.1;9.1 Surface Trend and Target Prediction;185
11.2;9.2 Determination of the Next Viewpoint;191
11.3;9.3 Simulation;194
11.4;9.4 Practical Implementation;199
11.5;9.5 Discussion and Conclusion;211
12;Chapter 10 Integrating Planning with Active Illumination;214
12.1;10.1 Introduction;214
12.2;10.2 From Human Vision to Machine Vision;216
12.3;10.3 Evaluation of Illumination Conditions;217
12.4;10.4 Controllable Things;219
12.5;10.5 Glare Avoidance;221
12.6;10.6 Intensity Estimation;223
12.7;10.7 Intensity Control;228
12.8;10.8 Simulation;231
12.9;10.9 Implementation;234
12.10;10.10 Summary;238
13;Bibliography;239
14;Index;266