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Grid-based Environment Estimation for Local Autonomous Vehicle Navigation / Titelei/Inhaltsverzeichnis
Grid-based Environment Estimation for Local Autonomous Vehicle Navigation / Titelei/Inhaltsverzeichnis
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Chapter
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I–XIV
Titelei/Inhaltsverzeichnis
I–XIV
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1–9
1 Introduction
1–9
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1.1 Prior Knowledge for Autonomous Navigation in Road Scenarios
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1.1.1 High Prior Knowledge Navigation
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1.1.2 Sensor-based Navigation
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1.1.3 Low Prior Knowledge Navigation
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1.2 EnvironmentModel
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1.3 Main Contributions and Outline of the Thesis
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10–39
2 Grid-based Tracking and Mapping
10–39
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2.1 Introduction
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2.1.1 State of the Art
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2.1.2 Approach and Contribution
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2.2 Fundamentals
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2.2.1 Dempster–Shafer EnvironmentModel
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2.2.2 GTAMOverview
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2.2.3 Scan Grid Generation and Fusion
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2.3 The ParticleMap
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2.3.1 Estimating Cell Velocity Distributions using Particle Filters
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2.3.2 Particle Creation and Sampling
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2.3.3 ParticleWeighting and Resampling
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2.3.4 BeliefMass Derivation
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2.4 The Dempster–Shafer TheoryMap
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2.4.1 Filtering over Time
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2.4.2 Deriving Static BayesianMaps
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2.5 Results
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2.6 Summary
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40–63
3 Detection of Principal Moving Directions
40–63
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3.1 Introduction
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3.1.1 State of the Art
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3.1.2 Approach and Contribution
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3.2 Local Path Planning with Unknown Goal Poses
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3.2.1 ProblemFormulation
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3.2.2 Velocity-dependent Reachability Graph
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3.2.3 Path Cost and Heuristic with Unknown Goal Poses
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3.2.4 A*-RRTMotion Primitive Path Planner
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3.3 Environment-based Trajectory Clustering
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3.3.1 Equivalence for Local Trajectories
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3.3.2 Clustering with a Binary Equivalence Predicate
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3.3.3 Trajectory Clustering with Overlapping Clusters
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3.4 Results
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3.5 Summary
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64–86
4 Configuration Space Costs: Cost Evaluation on Workspace Cost Maps
64–86
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4.1 Introduction
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4.1.1 State of the Art
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4.1.2 Approach and Contribution
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4.2 Grid-based Collision Checking and Cost Evaluation
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4.2.1 Collision Checking Fundamentals
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4.2.2 Extending Collision Checking to Cost Evaluation
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4.3 Fast Approximate Calculation of the Configuration Space Costs for Arbitrary Footprints with FAMOD
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4.3.1 Calculation of Grayscale Dilation with Convolution
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4.3.2 Practical Considerations
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4.4 Efficient Exact Calculation of the Configuration Space Costs for Rectangular Footprints with vHGW-360
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4.4.1 Reducing Computations by Exploiting Symmetry
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4.4.2 The vHGWAlgorithm
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4.4.3 Practical Considerations
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4.5 Evaluating Continuous Paths on Discrete Grids
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4.5.1 Calculating Path Costs with the Configuration Space Costs
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4.5.2 Determining Look-Up Positions
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4.6 Results
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4.7 Summary
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87–101
5 Road Course and Road Boundary Estimation
87–101
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5.1 Introduction
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5.1.1 State of the Art
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5.1.2 Approach and Contribution
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5.2 Overview
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5.3 Path-based Road Boundary Estimation
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5.3.1 The Effects of Path Clustering
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5.3.2 Road Boundary Estimation
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5.4 Road Course Validation
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5.4.1 Single Frame Validation
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5.4.2 Recursive Bayesian Validation
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5.5 Road Course Tracking
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5.5.1 Tracking Road Courses based on Paths
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5.5.2 Path Association
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5.6 Results
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5.7 Summary
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102–122
6 Evaluation
102–122
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6.1 Evaluation of the Grid-based Tracking and Mapping
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6.1.1 Particle Convergence with Static Particle Sampling
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6.1.2 Parameter Evaluation
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6.1.3 Classification
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6.1.4 Estimated Velocities
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6.2 Evaluation of the Road Course Estimation
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6.2.1 Road Course Validation
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6.2.2 Boundary Estimation Accuracy
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6.2.3 Comparison to Predicted Vehicle Path
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6.2.4 Autonomous Navigation in an Unmapped Road Scenario
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6.3 Qualitative Evaluation of the Road Course Estimation with GTAM
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6.4 Summary
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123–126
7 Conclusion
123–126
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127–135
A Appendix
127–135
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A.1 Prototype Vehicle and Sensor Setup
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A.2 Hardware and Software Computing Platform
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A.3 Local GridMapping
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A.4 Path Smoothing
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A.5 Data Sets
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A.5.1 Grid-based Tracking andMapping
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A.5.2 Road Course Estimation
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136–136
Own Publications
136–136
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137–154
Bibliography
137–154
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Grid-based Environment Estimation for Local Autonomous Vehicle Navigation , page I - XIV
Titelei/Inhaltsverzeichnis
Autoren
Georg Tanzmeister
DOI
doi.org/10.51202/9783186246080-I
ISBN print: 978-3-18-524608-1
ISBN online: 978-3-18-624608-0
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