Robotic Navigation and Mapping with Radar

Name: Robotic Navigation and Mapping with Radar
Brand: Artech House
Price: 131.99 EUR
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Martin Adams(Author)

Artech House (Publisher)

1st Edition

Published on 1. January 2012

330 pages

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978-1-60807-483-9 (ISBN)

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Robotic Navigation and Mapping with Radar
Contents
Preface
Acknowledgments
Acronyms
Nomenclature
Chapter 1 Introduction
1.1 Isn't Navigation and Mapping with Radar Solved?
1.1.1 Applying Missile/Aircraft Guidance Technologies to Robotic Vehicles
1.1.2 Placing Autonomous Navigation of Robotic Vehicles into Perspective
1.2 Why Radar in Robotics? Motivation
1.3 The Direction of Radar-based Robotics Research
1.3.1 Mining Applications
1.3.2 Intelligent Transportation System Applications
1.3.3 Land-Based SLAM Applications
1.3.4 Coastal Marine Applications
1.4 Structure of the Book
References
PART I: Fundamentals of Radar and Robotic Navigation
Chapter 2 A Brief Overview of Radar Fundamentals
2.1 Introduction
2.2 Radar Measurements
2.3 The Radar Equation
2.4 Radar Signal Attenuation
2.5 Measurement Power Compression and Range Compensation
2.5.1 Logarithmic Compression
2.5.2 Range Compensation
2.5.3 Logarithmic Compression and Range Compensation During Target Absence
2.5.4 Logarithmic Compression and Range Compensation During Target Presence
2.6 Radar-Range Measurement Techniques
2.6.1 Time-of-Flight (TOF) Pulsed Radar
2.6.2 Frequency Modulated Continuous Wave (FMCW) Radar
2.6.2.2 Doppler Measurements
2.6.2.3 Multiple Line-of-Sight Targets
2.7 Sources of Uncertainty in Radar
2.7.1 Sources of Uncertainty Common to All Radar Types
2.8 Uncertainty Specific to TOF and FMCW Radar
2.8.1 Uncertainty in TOF Radars
2.8.2 Uncertainty in FMCW Radars
2.9 Polar to Cartesian Data Transformation
2.9.1 Nearest Neighbor Polar to Cartesian Data Conversion
2.9.2 Weighted Polar to Cartesian Data Conversion
2.10 Summary
2.11 Bibliographical Remarks
2.11.1 Extensions to the Radar Equation
2.11.2 Signal Propagation/Attenuation
2.11.3 Range Measurement Methods
2.11.4 Uncertainty in Radar
References
Chapter 3 An Introduction to Detection Theory
3.1 Introduction
3.2 Concepts of Detection Theory
3.3 Different Approaches to Target Detection
3.3.1 Non-adaptive Detection
3.3.2 Hypothesis Free Modeling
3.3.3 Stochastic-Based Adaptive Detection
3.4 Detection Theory with Known Noise Statistics
3.4.1 Constant CFARPfa with Known Noise Statistics
3.4.2 Probability of Detection CFARPD with Known Noise Statistics
3.4.3 Probabilities of Missed Detection CFARPMD and Noise CFARPn with Known Noise Statistics
3.5 Detection with Unknown Noise Statistics-Adaptive CFAR Processors
3.5.1 Cell Averaging-CA-CFAR Processors
3.5.2 Ordered Statistics-OS-CFAR Processors
3.6 Summary
3.7 Bibliographical Remarks
References
Chapter 4 Robotic Navigation and Mapping
4.1 Introduction
4.2 General Bayesian SLAM-The Joint Problem
4.2.1 Vehicle State Representation
4.2.2 Map Representation
4.3 Solving Robot Mapping and Localization Individually
4.3.1 Probabilistic Robotic Mapping
4.3.2 Probabilistic Robotic Localization
4.4 Popular Robotic Mapping Solutions
4.4.1 Grid-Based Robotic Mapping (GBRM)
4.4.2 Feature-Based Robotic Mapping (FBRM)
4.5 Relating Sensor Measurements to Robotic Mapping and SLAM
4.5.1 Relating the Spatial Measurement Interpretation to the Mapping/SLAM State
4.5.2 Relating the Detection Measurement Interpretation to the Mapping/SLAM State
4.6 Popular FB-SLAM Solutions
4.6.1 Bayesian FB-SLAM-Approximate Gaussian Solutions
4.6.2 Feature Association
4.6.3 Bayesian FB-SLAM-Approximate Particle Solutions
4.6.4 A Factorized Solution to SLAM (FastSLAM)
4.6.5 Multi-Hypothesis (MH) FastSLAM
4.6.6 General Comments on Vector-Based FB SLAM
4.7 FBRM and SLAM with Random Finite Sets
4.7.1 Motivation: Why Random Finite Sets
4.7.2 RFS Representations of State and Detected Features
4.7.3 Bayesian Formulation with a Finite Set Feature Map
4.7.4 The Probability Hypothesis Density (PHD) Estimator
4.7.5 The PHD Filter
4.8 SLAM and FBRM Performance Metrics
4.8.1 Vehicle State Estimate Evaluation
4.8.2 Map Estimate Evaluation
4.8.3 Evaluation of FBRM and SLAM with the Second Order Wasserstein Metric
4.9 Summary
4.10 Bibliographical Remarks
4.10.1 Grid-Based Robotic Mapping (GBRM
4.10.2 Gaussian Approximations to Bayes Theorem
4.10.3 Non-Parametric Approximations to Bayesian FB-SLAM
4.10.4 Other Approximations to Bayesian FB-SLAM
4.10.5 Feature Association and Management
4.10.6 Random Finite Sets (RFSs)
4.10.7 SLAM and FBRM Evaluation Metrics
References
Part II: Radar Modeling and Scan Integration
Chapter 5 Predicting and Simulating FMCW Radar Measurements
5.1 Introduction
5.2 FMCW Radar Detection in the Presence of Noise
5.3 Noise Distributions During Target Absence and Presence
5.3.1 Received Power Noise Estimation
5.3.2 Range Noise Estimation
5.4 Predicting Radar Measurements
5.4.1 A-Scope Prediction Based on Expected Target RCS and Range
5.4.2 A-Scope Prediction Based on Robot Motion
5.5 Quantitative Comparison of Predicted and Actual Measurements
5.6 A-scope Prediction Results
5.6.1 Single Bearing A-Scope Prediction
5.6.2 360° Scan Multiple A-Scope Prediction, Based on Robot Motion
5.7 Summary
5.8 Bibliographical Remarks
References
Chapter 6 Reducing Detection Errors and Noise with Multiple Radar Scans
6.1 Introduction
6.2 Radar Data in an Urban Environment
6.2.1 Landmark Detection with Single Scan CA-CFAR
6.3 Classical Scan Integration Methods
6.3.1 Coherent and Noncoherent Integration
6.3.2 Binary Integration Detection
6.4 Integration Based on Target Presence Probability (TPP) Estimation
6.5.4 Numerical Method for Determining T TPP (ap ,l ) and TPP
6.5.1 TPP Response to Noise: TPP
6.5.2 TPP Response to a Landmark and Noise: TPP
6.5.3 Choice of ap, TTPP (ap, l ) and l
6.6 A Comparison of Scan Integration Methods
6.7 A Note on Multi-Path Reflections
6.8 TPP Integration of Radar in an Urban Environment
6.8.1 Qualitative Assessment of TPP Applied to A-Scope Information
6.8.2 Quantitative Assessment of TPP Applied to Complete Scans
6.8.3 A Qualitative Assessment of an Entire Parking Lot Scene
6.9 Recursive A-Scope Noise Reduction
6.9.1 Single A-Scope Noise Subtraction
6.9.2 Multiple A-Scope-Complete Scan Noise Subtraction
6.10 Summary
6.11 Bibliographical Remarks
References
Part III: IRobotic Mapping with KnownVehicle Location
Chapter 7 Grid-Based Robotic Mapping with Detection Likelihood Filtering
7.1 Introduction
7.2 The Grid-Based Robotic Mapping (GBRM) Problem
7.2.1 GBRM Based on Range Measurements
7.2.2 GBRM with Detection Measurements
7.2.3 Detection versus Range Measurement Models
7.3 Mapping with Unknown Measurement Likelihoods
7.3.1 Data Format
7.3.2 GBRM Algorithm Overview
7.3.3 Constant False Alarm Rate (CFAR) Detector
7.3.4 Map Occupancy and Detection Likelihood Estimator
7.3.5 Incorporation of the OS-CFAR Processor
7.4 GBRM-ML Particle Filter Implementation
7.5 Comparisons of Detection and Spatial-Based GBRM
7.5.1 Dataset 1: Synthetic Data, Single Landmark
7.5.2 Dataset 2: Real Experiments in the Parking Lot Environment
7.5.3 Dataset 3: A Campus Environment
7.6 Summary
7.7 Bibliographical Remarks
References
Chapter 8 Feature-Based Robotic Mapping with Random Finite Sets
8.1 Introduction
8.2 The Probability Hypothesis Density (PHD)-FBRM Filter
8.3 PHD-FBRM Filter Implementation
8.3.1 The FBRM New Feature Proposal Strategy
8.3.2 Gaussian Management and State Estimation
8.3.3 GMM-PHD-FBRM Pseudo Code
8.4 PHD-FBRM Computational Complexity
8.5 Analysis of the PHD-FBRM Filter
8.6 Summary
8.7 Bibliographical Remarks
References
Part IV Simultaneous Localization and Mapping
Chapter 9 Radar-Based SLAM with Random Finite Sets
9.1 Introduction
9.2 Slam with the PHD Filter
9.2.1 The Factorized RFS-SLAM Recursion
9.2.2 PHD Mapping-Rao-Blackwellization
9.2.3 PHD-SLAM
9.3 Implementing the RB-PHD-SLAM Filter
9.3.1 PHD Mapping-Implementation
9.3.2 The Vehicle Trajectory-Implementation
9.3.3 Estimating the Map
9.3.4 GMM-PHD-SLAM Pseudo Code
9.4 RB-PHD-Slam Computational Complexity
9.5 Radar-Based Comparisons of RFS and Vector-Based SLAM
9.6 Summary
9.7 Bibliographical Remarks
References
Chapter 10 X-Band Radar-Based SLAM in an Off-Shore Environment
10.1 Introduction
10.2 The ASC and the Coastal Environment
10.3 Marine Radar Feature Extraction
10.3.1 Adaptive Coastal Feature Detection-OS-CFAR
10.3.2 Image-Based Smoothing-Gaussian Filtering
10.3.3 Image-Based Thresholding
10.3.4 Image-Based Clustering
10.3.5 Feature Labeling
10.4 The Marine-Based SLAM Algorithms
10.4.1 The ASC Process Model
10.4.2 RFS SLAM with the PHD Filter
10.4.3 NN-EKF-SLAM Implementation
10.4.4 Multi-Hypothesis (MH) FastSLAM Implementation
10.5 Comparisons of SLAM Concepts at Sea
10.5.1 SLAM Trial 1-Comparing PHD and NN-EKF-SLAM
10.5.2 SLAM Trial 2-Comparing RB-PHD-SLAM and MH-FastSLAM
10.6 Summary
10.7 Bibliographical Remarks
References
Appendix A: The Navtech FMCW MMW Radar Specifications
Appendix B: Derivation of g(Zk|Zk-1, Xk) for the RB-PHD-SLAM Filter
B.1 The Empty Strategy
B.2 The Single Feature Strategy
Appendix C: NN-EKF and FastSLAM FeatureManagement
Index

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