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Grid-Based Object Tracking / Titelei/Inhaltsverzeichnis
Grid-Based Object Tracking / Titelei/Inhaltsverzeichnis
Contents
Chapter
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Page
I–XII
Titelei/Inhaltsverzeichnis
I–XII
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1–12
1 Introduction
1–12
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1.1 Motivation
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1.2 Challenges of Multi-Sensor Environment Perception
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1.3 Main Contribution and Outline of This Work
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13–38
2 Measurement Grid Representation and Fusion
13–38
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2.1 Introduction
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2.1.1 Related Work
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2.1.2 Contribution and Outline
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2.2 Evidential Occupancy Grid Representation
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2.2.1 Spatial Grid Structure
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2.2.2 Evidential Occupancy Representation
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2.3 Sensor Measurement Grids
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2.3.1 Generic Position-Based Evidential Occupancy Grid Derivation
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2.3.2 Lidar Measurement Grids
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2.3.3 Radar Measurement Grids
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2.3.4 Camera Measurement Grids
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2.4 Measurement Grid Fusion
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2.4.1 Basic Cell-Wise Fusion of Evidence Masses
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2.4.2 Spatiotemporal Alignment of Asynchronous Sensor Data
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2.5 Results and Summary
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39–78
3 Dynamic Grid Mapping and Particle Tracking
39–78
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3.1 Introduction
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3.1.1 Related Work
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3.1.2 Contribution and Outline
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3.2 Dynamic Grid Map and Particle Representation
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3.2.1 Evidential Frame of Discernment for Dynamic Environments
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3.2.2 Dynamic Grid Map Representation
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3.2.3 Low-Level Particle Representation
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3.3 Particle-Based Prediction of the Dynamic Grid Map
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3.3.1 Prediction of the Dynamic Evidence Mass
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3.3.2 Prediction of the Non-Dynamic Evidence Masses
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3.3.3 Resulting Combined Predicted Dynamic Grid Map
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3.4 Measurement Update of the Dynamic Grid Map
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3.4.1 Conflict Assignment
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3.4.2 Occupancy Differentiation from Distance-Only Measurements
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3.4.3 Additional Radar- and Camera-Based Occupancy Classification
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3.4.4 Adapted Occupancy Convergence by Object Tracking Feedback
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3.4.5 Overall Resulting Updated Evidence Masses of the Map
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3.5 Weighting and Resampling of the Particle Population
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3.5.1 Cell-Wise Occupancy-Based Number of Desired Particles
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3.5.2 Radar- and Camera-Based Particle Velocity Weighting
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3.5.3 Initialization of New Particles
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3.5.4 Resampling of the Particle Population
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3.6 Augmented Measurement Grid
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3.7 Results and Summary
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79–100
4 Object Extraction and Association
79–100
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4.1 Introduction
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4.1.1 Related Work
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4.1.2 Contribution and Outline
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4.2 Overview of the Extraction and Association Strategies
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4.2.1 Object Detection Based on Dynamic Occupancy Classification
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4.2.2 Measurement Abstraction Levels of the Association Problem
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4.3 Cell Association for Existing Object Tracks
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4.3.1 Association Based on Predicted High-Level Object Track
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4.3.2 Particle Labeling Association
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4.3.3 Additional Clustering with Verification
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4.4 Extraction of Newly Occurring Object Tracks
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4.4.1 Density-Based Clustering of Dynamic Occupied Cells
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4.4.2 Additional Region Growing with Velocity Variance Analysis
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4.5 Results and Summary
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101–126
5 Object State Estimation
101–126
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5.1 Introduction
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5.1.1 Related Work
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5.1.2 Contribution and Outline
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5.2 Object State Representation
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5.3 Dynamic State Estimation
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5.3.1 Prediction
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5.3.2 Transformation of Associated Cells to the Box Representation
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5.3.3 Position Measurements with Reference Point Selection
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5.3.4 Velocity and Orientation Estimation by the Particle Tracking
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5.3.5 Orientation Estimation Based on Freespace Evidence
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5.4 Additional Radar-Based Dynamic Estimation
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5.4.1 Association of Radar Doppler Measurements
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5.4.2 Geometric Relations of the Radial Velocity Component
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5.4.3 Radar-Based Motion Estimation
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5.5 Shape Estimation and Object Classification
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5.5.1 Histogram Filter Geometry Distribution Estimation
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5.5.2 Classification Based on Geometry and Velocity Information
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5.5.3 Combined Object Classification with Camera Information
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5.5.4 Extraction of Estimated Length and Width of Box Model
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5.6 Results and Summary
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127–164
6 Evaluation
127–164
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6.1 Overview
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6.1.1 Sensor Setup
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6.1.2 Main Processing Steps of this Work
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6.1.3 Primary Grid Configuration and Algorithm Implementation
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6.2 Dynamic Occupancy Grid Estimation
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6.2.1 Accumulation over Time
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6.2.2 Comparison with Original Approach
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6.2.3 Occupancy Classification with Additional Information
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6.3 Object Detection and Tracking
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6.3.1 Object Extraction and Association
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6.3.2 Dynamic State Estimation for Highly Dynamic Maneuvers
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6.3.3 Object Shape Estimation and Classification
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6.4 Summary and Overall Approach Application
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165–168
7 Conclusion
165–168
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169–169
Own Publications
169–169
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170–186
Bibliography
170–186
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Grid-Based Object Tracking , page I - XII
Titelei/Inhaltsverzeichnis
Autoren
Sascha Steyer
DOI
doi.org/10.51202/9783186272089-I
ISBN print: 978-3-18-527208-0
ISBN online: 978-3-18-627208-9
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doi.org/10.51202/9783186272089-I
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