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Hierarchical Voronoi Graphs: Spatial Representation and Reasoning for Mobile Robots 2010 ed. [Kõva köide]

  • Formaat: Hardback, 218 pages, kõrgus x laius: 235x155 mm, kaal: 580 g, XXIII, 218 p., 1 Hardback
  • Ilmumisaeg: 15-Dec-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642103022
  • ISBN-13: 9783642103025
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  • Formaat: Hardback, 218 pages, kõrgus x laius: 235x155 mm, kaal: 580 g, XXIII, 218 p., 1 Hardback
  • Ilmumisaeg: 15-Dec-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642103022
  • ISBN-13: 9783642103025
Teised raamatud teemal:
What is space? Is there space when there are objects to occupy it or is there space only when there are no objects to occupy it? Can there be space without objects? These are old philosophical questions that concern the ontology of space in the philosophical sense of ontology what is the nature of space? Cognitive science in general and arti cial intelligence in particular are less c- cerned with the nature of things than with their mental conceptualizations. In spatial cognition research we address questions like What do we know about space? How is space represented? What are the representational entities? What are the rep- sentational structures? Answers to these questions are described in what is called ontologies in arti cial intelligence. Different tasks require different knowledge, and different representations of knowledge facilitate different ways of solving problems. In this book, Jan Oliver Wallgrün develops and investigates representational structures to support tasks of autonomous mobile robots, from the acquisition of knowledge to the use of this knowledge for navigation. The research presented is concerned with the robot mapping problem, the pr- lem of building a spatial representation of an environment that is perceived by s- sors that only provide incomplete and uncertain information; this information usually needs to be related to other imprecise or uncertain information. The routes a robot can take can be abstractly described in terms of graphs where alternative routes are represented by alternative branches in these route graphs.
1 Introduction 1
1.1 The Robot Mapping Problem
2
1.2 The Spatial Representation Perspective
3
1.3 The Uncertainty Handling Perspective
3
1.4 Combining Representation and Uncertainty Handling
4
1.5 Route Graphs Based on Generalized Voronoi Diagrams
5
1.6 Theses, Goals, and Contributions of This Book
6
1.7 Outline of This Book
8
2 Robot Mapping 11
2.1 A Spatial Model for What?
14
2.1.1 Navigation
14
2.1.2 Systematic Exploration
16
2.1.3 Communication
16
2.2 Correctness, Consistency, and Criteria
17
2.2.1 Extractability and Maintainability
18
2.2.2 Information Adequacy
18
2.2.3 Efficiency and Scalability
18
2.3 Spatial Representation and Organization
19
2.3.1 Basic Spatial Representation Approaches
19
2.3.2 Coordinate-Based Representations
20
2.3.3 Relational Representations
26
2.3.4 Organizational Forms
31
2.4 Uncertainty Handling Approaches
36
2.4.1 Incremental Approaches
37
2.4.2 Multi-pass Approaches
41
2.5 Conclusions
42
3 Voronoi-Based Spatial Representations 45
3.1 Voronoi Diagram and Generalized Voronoi Diagram
45
3.2 Generalized Voronoi Graph and Embedded Generalized Voronoi Graph
47
3.3 Annotated Generalized Voronoi Graphs
49
3.4 Hierarchical Annotated Voronoi Graphs
50
3.5 Partial and Local Voronoi Graphs
51
3.6 An Instance of the HAGVG
53
3.7 Stability Problems of Voronoi-Based Representations
54
3.8 Strengths and Weaknesses of the Representation
55
4 Simplification and Hierarchical Voronoi Graph Construction 59
4.1 Relevance Measures for Voronoi Nodes
60
4.2 Computation of Relevance Values
64
4.3 Voronoi Graph Simplification
69
4.4 HAGVG Construction
72
4.5 Admitting Incomplete Information
73
4.6 Improving the Efficiency of the Relevance Computation
75
4.7 Incremental Computation
80
4.8 Application Scenarios
82
4.8.1 Incremental HAGVG Construction
82
4.8.2 Removal of Unstable Parts
82
4.8.3 Automatic Route Graph Generation from Vector Data
82
5 Voronoi Graph Matching for Data Association 85
5.1 The Data Association Problem
85
5.1.1 Data Associations and the Interpretation Tree
86
5.1.2 Data Association Approaches
88
5.2 AGVG Matching Based on Ordered Tree Edit Distance
90
5.2.1 Ordered Tree Matching Based on Edit Distance
92
5.2.2 Overall Edit Distance
97
5.2.3 Modeling Removal and Addition Costs
98
5.2.4 Optimizations
99
5.2.5 Complexity
99
5.3 Incorporating Constraints
100
5.3.1 Unary Constraints Based on Pose Estimates and Node Similarity
101
5.3.2 Binary Constraints Based on Relative Distance
104
5.3.3 Ternary Angle Constraints
106
5.4 Map Merging Based on a Computed Data Association
109
6 Global Mapping: Minimal Route Graphs Under Spatial Constraints 113
6.1 Theoretical Problem
114
6.2 Branch and Bound Search for Minimal Model Finding
123
6.2.1 Search Through the Interpretation Tree
124
6.2.2 Best-First Branch and Bound Search Based on Solution Size
126
6.2.3 Expand and Update Operations
128
6.2.4 Two Variants of the Minimal Model Finding Problem
134
6.3 Pruning Based on Spatial Constraints
136
6.3.1 Checking Planarity
136
6.3.2 Checking Spatial Consistency
139
6.3.3 Incorporation into the Search Algorithm
143
6.4 Combining Minimal Route Graph Mapping and AGVG Representations
144
7 Experimental Evaluation 147
7.1 Relevance Assessment and HAGVG Construction
147
7.1.1 Efficiency of the Relevance Computation Algorithms
147
7.1.2 Combining the HAGVG Construction Methods with a Grid-Based FastSLAM Approach
150
7.2 Evaluation of the Voronoi-Based Data Association
152
7.3 Evaluation of the Minimal Route Graph Approach
156
7.3.1 Solution Quality
157
7.3.2 Pruning Efficiency
160
7.3.3 Absolute vs. Relative Direction Information
163
7.3.4 Overall Computational Costs
166
7.3.5 Application to Real AGVG Data
168
7.4 A Complete Multi-hypothesis Mapping System
170
7.4.1 Local Metric Mapping and Local AGVG Computation
170
7.4.2 Data Association for Node Tracking and History Generation
171
7.4.3 Global Mapping and Past-processing
171
7.4.4 Experiments
171
7.4.5 Discussion
172
8 Conclusions and Outlook 177
8.1 Summary and Conclusions
177
8.1.1 Extraction and HAGVG Construction
178
8.1.2 Data Association and Matching
179
8.1.3 Minimal Route Graph Model Finding
179
8.1.4 Complete Mapping Approaches
180
8.2 Outlook
181
8.2.1 Extensions of the Work Described in Chaps. 3-6
181
8.2.2 Combining Voronoi Graphs and Uncertainty Handling
182
8.2.3 Challenges for Voranoi-Based Representation Approaches
183
8.2.4 Challenges for Qualitative Spatial Reasoning
185
8.2.5 The Future: Towards Spatially Competent Mobile Robots
185
A Mapping as Probabilistic State Estimation 187
A.1 The Recursive Bayes Filter
188
A.2 Parametric Filters
190
A.2.1 Kalman Filter
190
A.2.2 Extended Kalman Filter
191
A.3 Nonparametric Filters
192
A.3.1 Particle Filter
192
A.3.2 Rao-Blackwellized Particle Filter and FastSLAM
193
B Qualitative Spatial Reasoning 195
B.1 Qualitative Constraint Calculi
195
B.2 Weak vs. Strong Operations
198
B.3 Constraint Networks and Consistency
198
B.4 Checking Consistency
200
Bibliography 203