1 Introduction |
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1.1 The Robot Mapping Problem |
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1.2 The Spatial Representation Perspective |
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1.3 The Uncertainty Handling Perspective |
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1.4 Combining Representation and Uncertainty Handling |
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1.5 Route Graphs Based on Generalized Voronoi Diagrams |
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1.6 Theses, Goals, and Contributions of This Book |
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2 Robot Mapping |
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2.1 A Spatial Model for What? |
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2.1.2 Systematic Exploration |
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2.2 Correctness, Consistency, and Criteria |
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2.2.1 Extractability and Maintainability |
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2.2.2 Information Adequacy |
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2.2.3 Efficiency and Scalability |
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2.3 Spatial Representation and Organization |
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2.3.1 Basic Spatial Representation Approaches |
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2.3.2 Coordinate-Based Representations |
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2.3.3 Relational Representations |
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2.3.4 Organizational Forms |
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2.4 Uncertainty Handling Approaches |
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2.4.1 Incremental Approaches |
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2.4.2 Multi-pass Approaches |
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3 Voronoi-Based Spatial Representations |
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3.1 Voronoi Diagram and Generalized Voronoi Diagram |
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3.2 Generalized Voronoi Graph and Embedded Generalized Voronoi Graph |
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3.3 Annotated Generalized Voronoi Graphs |
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3.4 Hierarchical Annotated Voronoi Graphs |
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3.5 Partial and Local Voronoi Graphs |
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3.6 An Instance of the HAGVG |
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3.7 Stability Problems of Voronoi-Based Representations |
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3.8 Strengths and Weaknesses of the Representation |
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4 Simplification and Hierarchical Voronoi Graph Construction |
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4.1 Relevance Measures for Voronoi Nodes |
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4.2 Computation of Relevance Values |
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4.3 Voronoi Graph Simplification |
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4.5 Admitting Incomplete Information |
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4.6 Improving the Efficiency of the Relevance Computation |
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4.7 Incremental Computation |
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4.8 Application Scenarios |
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4.8.1 Incremental HAGVG Construction |
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4.8.2 Removal of Unstable Parts |
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4.8.3 Automatic Route Graph Generation from Vector Data |
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5 Voronoi Graph Matching for Data Association |
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5.1 The Data Association Problem |
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5.1.1 Data Associations and the Interpretation Tree |
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5.1.2 Data Association Approaches |
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5.2 AGVG Matching Based on Ordered Tree Edit Distance |
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5.2.1 Ordered Tree Matching Based on Edit Distance |
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5.2.2 Overall Edit Distance |
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5.2.3 Modeling Removal and Addition Costs |
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5.3 Incorporating Constraints |
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5.3.1 Unary Constraints Based on Pose Estimates and Node Similarity |
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5.3.2 Binary Constraints Based on Relative Distance |
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5.3.3 Ternary Angle Constraints |
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5.4 Map Merging Based on a Computed Data Association |
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6 Global Mapping: Minimal Route Graphs Under Spatial Constraints |
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6.2 Branch and Bound Search for Minimal Model Finding |
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6.2.1 Search Through the Interpretation Tree |
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6.2.2 Best-First Branch and Bound Search Based on Solution Size |
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6.2.3 Expand and Update Operations |
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6.2.4 Two Variants of the Minimal Model Finding Problem |
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6.3 Pruning Based on Spatial Constraints |
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6.3.2 Checking Spatial Consistency |
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6.3.3 Incorporation into the Search Algorithm |
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6.4 Combining Minimal Route Graph Mapping and AGVG Representations |
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7 Experimental Evaluation |
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7.1 Relevance Assessment and HAGVG Construction |
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7.1.1 Efficiency of the Relevance Computation Algorithms |
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7.1.2 Combining the HAGVG Construction Methods with a Grid-Based FastSLAM Approach |
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7.2 Evaluation of the Voronoi-Based Data Association |
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7.3 Evaluation of the Minimal Route Graph Approach |
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7.3.3 Absolute vs. Relative Direction Information |
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7.3.4 Overall Computational Costs |
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7.3.5 Application to Real AGVG Data |
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7.4 A Complete Multi-hypothesis Mapping System |
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7.4.1 Local Metric Mapping and Local AGVG Computation |
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7.4.2 Data Association for Node Tracking and History Generation |
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7.4.3 Global Mapping and Past-processing |
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8 Conclusions and Outlook |
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8.1 Summary and Conclusions |
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8.1.1 Extraction and HAGVG Construction |
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8.1.2 Data Association and Matching |
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8.1.3 Minimal Route Graph Model Finding |
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8.1.4 Complete Mapping Approaches |
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8.2.1 Extensions of the Work Described in Chaps. 3-6 |
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8.2.2 Combining Voronoi Graphs and Uncertainty Handling |
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8.2.3 Challenges for Voranoi-Based Representation Approaches |
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8.2.4 Challenges for Qualitative Spatial Reasoning |
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8.2.5 The Future: Towards Spatially Competent Mobile Robots |
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A Mapping as Probabilistic State Estimation |
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A.1 The Recursive Bayes Filter |
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A.2.2 Extended Kalman Filter |
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A.3 Nonparametric Filters |
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A.3.2 Rao-Blackwellized Particle Filter and FastSLAM |
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B Qualitative Spatial Reasoning |
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B.1 Qualitative Constraint Calculi |
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B.2 Weak vs. Strong Operations |
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B.3 Constraint Networks and Consistency |
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Bibliography |
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