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E-raamat: Robotic Navigation and Mapping with Radar

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  • Formaat: 330 pages
  • Ilmumisaeg: 31-Jan-2012
  • Kirjastus: Artech House Publishers
  • ISBN-13: 9781608074839
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Robotic Navigation and Mapping with RadarSuurem pilt
  • Formaat: 330 pages
  • Ilmumisaeg: 31-Jan-2012
  • Kirjastus: Artech House Publishers
  • ISBN-13: 9781608074839
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Robotics engineers Adams (U. of Chile), John Mullane, and Ebi Jose (both with a Singapore company) and tracking specialist Ba-Ngu Vo (U. of Western Australia) seek to bridge the gap between the robotics and radar communities by showing how to apply radar to robotic vehicle navigation. They cover fundamentals of radar and robotic navigation, radar modeling and scan integration, robotic mapping with known vehicle location, and simultaneous localization and mapping. Among specific topics are detection theory, reducing detection errors and noise with multiple radar scans, grid-based robotic mapping with detection likelihood filtering, and feature-based robotic mapping with random finite sets. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)
Preface xiii
Acknowledgments xv
Acronyms xvii
Nomenclature xxi
Chapter 1 Introduction
1(32)
1.1 Isn't Navigation and Mapping with Radar Solved?
1(11)
1.1.1 Applying Missile/Aircraft Guidance Technologies to Robotic Vehicles
2(9)
1.1.2 Placing Autonomous Navigation of Robotic Vehicles into Perspective
11(1)
1.2 Why Radar in Robotics? Motivation
12(7)
1.3 The Direction of Radar-Based Robotics Research
19(8)
1.3.1 Mining Applications
19(2)
1.3.2 Intelligent Transportation System Applications
21(3)
1.3.3 Land-Based SLAM Applications
24(1)
1.3.4 Coastal Marine Applications
25(2)
1.4 Structure of the Book
27(6)
References
28(5)
PART I Fundamentals of Radar and Robotic Navigation
33(130)
Chapter 2 A Brief Overview of Radar Fundamentals
35(46)
2.1 Introduction
35(1)
2.2 Radar Measurements
36(2)
2.3 The Radar Equation
38(2)
2.4 Radar Signal Attenuation
40(3)
2.5 Measurement Power Compression and Range Compensation
43(8)
2.5.1 Logarithmic Compression
44(1)
2.5.2 Range Compensation
44(1)
2.5.3 Logarithmic Compression and Range Compensation During Target Absence
45(2)
2.5.4 Logarithmic Compression and Range Compensation During Target Presence
47(4)
2.6 Radar-Range Measurement Techniques
51(7)
2.6.1 Time-of-Flight (TOF) Pulsed Radar
51(2)
2.6.2 Frequency Modulated, Continuous Wave (FMCW) Radar
53(5)
2.7 Sources of Uncertainty in Radar
58(10)
2.7.1 Sources of Uncertainty Common to All Radar Types
59(9)
2.8 Uncertainty Specific to TOF and FMCW Radar
68(4)
2.8.1 Uncertainty in TOF Radars
68(2)
2.8.2 Uncertainty in FMCW Radars
70(2)
2.9 Polar to Cartesian Data Transformation
72(4)
2.9.1 Nearest Neighbor Polar to Cartesian Data Conversion
73(1)
2.9.2 Weighted Polar to Cartesian Data Conversion
73(3)
2.10 Summary
76(1)
2.11 Bibliographical Remarks
76(5)
2.11.1 Extensions to the Radar Equation
76(1)
2.11.2 Signal Propagation/Attenuation
77(1)
2.11.3 Range Measurement Methods
78(1)
2.11.4 Uncertainty in Radar
78(1)
References
79(2)
Chapter 3 An Introduction to Detection Theory
81(24)
3.1 Introduction
81(1)
3.2 Concepts of Detection Theory
82(2)
3.3 Different Approaches to Target Detection
84(3)
3.3.1 Non-adaptive Detection
84(1)
3.3.2 Hypothesis Free Modeling
85(1)
3.3.3 Stochastic-Based Adaptive Detection
86(1)
3.4 Detection Theory with Known Noise Statistics
87(2)
3.4.1 Constant PCFAR with Known Noise Statistics
87(1)
3.4.2 Probability of Detection PDCFAR with Known Noise Statistics
88(1)
3.4.3 Probabilities of Missed Detection PMDCFAR and Noise PnCFAR with Known Noise Statistics
89(1)
3.5 Detection with Unknown Noise Statistics---Adaptive CFAR Processors
89(11)
3.5.1 Cell Averaging---CA-CFAR Processors
90(4)
3.5.2 Ordered Statistics---OS-CFAR Processors
94(6)
3.6 Summary
100(1)
3.7 Bibliographical Remarks
101(4)
References
102(3)
Chapter 4 Robotic Navigation and Mapping
105(58)
4.1 Introduction
105(2)
4.2 General Bayesian SLAM---The Joint Problem
107(8)
4.2.1 Vehicle State Representation
109(3)
4.2.2 Map Representation
112(3)
4.3 Solving Robot Mapping and Localization Individually
115(2)
4.3.1 Probabilistic Robotic Mapping
116(1)
4.3.2 Probabilistic Robotic Localization
116(1)
4.4 Popular Robotic Mapping Solutions
117(3)
4.4.1 Grid-Based Robotic Mapping (GBRM)
117(1)
4.4.2 Feature-Based Robotic Mapping (FBRM)
118(2)
4.5 Relating Sensor Measurements to Robotic Mapping and SLAM
120(4)
4.5.1 Relating the Spatial Measurement Interpretation to the Mapping/SLAM State
121(1)
4.5.2 Relating the Detection Measurement Interpretation to the Mapping/SLAM State
122(2)
4.6 Popular FB-SLAM Solutions
124(9)
4.6.1 Bayesian FB-SLAM---Approximate Gaussian Solutions
124(2)
4.6.2 Feature Association
126(2)
4.6.3 Bayesian FB-SLAM---Approximate Particle Solutions
128(1)
4.6.4 A Factorized Solution to SLAM (FastSLAM)
129(1)
4.6.5 Multi-Hypothesis (MH) FastSLAM
130(1)
4.6.6 General Comments on Vector-Based FB SLAM
130(3)
4.7 FBRM and SLAM with Random Finite Sets
133(12)
4.7.1 Motivation: Why Random Finite Sets
133(2)
4.7.2 RFS Representations of State and Detected Features
135(2)
4.7.3 Bayesian Formulation with a Finite Set Feature Map
137(1)
4.7.4 The Probability Hypothesis Density (PHD) Estimator
138(4)
4.7.5 The PHD Filter
142(3)
4.8 SLAM and FBRM Performance Metrics
145(3)
4.8.1 Vehicle State Estimate Evaluation
145(1)
4.8.2 Map Estimate Evaluation
145(1)
4.8.3 Evaluation of FBRM and SLAM with the Second Order Wasserstein Metric
146(2)
4.9 Summary
148(1)
4.10 Bibliographical Remarks
149(14)
4.10.1 Grid-Based Robotic Mapping (GBRM)
149(1)
4.10.2 Gaussian Approximations to Bayes Theorem
150(2)
4.10.3 Non-Parametric Approximations to Bayesian FB-SLAM
152(1)
4.10.4 Other Approximations to Bayesian FB-SLAM
152(3)
4.10.5 Feature Association and Management
155(1)
4.10.6 Random Finite Sets (RFSs)
156(1)
4.10.7 SLAM and FBRM Evaluation Metrics
156(1)
References
157(6)
PART II Radar Modeling and Scan Integration
163(70)
Chapter 5 Predicting and Simulating FMCW Radar Measurements
165(30)
5.1 Introduction
165(1)
5.2 FMCW Radar Detection in the Presence of Noise
166(2)
5.3 Noise Distributions During Target Absence and Presence
168(5)
5.3.1 Received Power Noise Estimation
168(1)
5.3.2 Range Noise Estimation
169(4)
5.4 Predicting Radar Measurements
173(3)
5.4.1 A-Scope Prediction Based on Expected Target RCS and Range
173(1)
5.4.2 A-Scope Prediction Based on Robot Motion
174(2)
5.5 Quantitative Comparison of Predicted and Actual Measurements
176(1)
5.6 A-scope Prediction Results
177(11)
5.6.1 Single Bearing A-Scope Prediction
177(2)
5.6.2 360° Scan Multiple A-Scope Prediction, Based on Robot Motion
179(9)
5.7 Summary
188(4)
5.8 Bibliographical Remarks
192(3)
References
193(2)
Chapter 6 Reducing Detection Errors and Noise with Multiple Radar Scans
195(38)
6.1 Introduction
195(1)
6.2 Radar Data in an Urban Environment
196(2)
6.2.1 Landmark Detection with Single Scan CA-CFAR
198(1)
6.3 Classical Scan Integration Methods
198(6)
6.3.1 Coherent and Noncoherent Integration
198(3)
6.3.2 Binary Integration Detection
201(3)
6.4 Integration Based on Target Presence Probability (TPP) Estimation
204(2)
6.5 False Alarm and Detection Probabilities for the TPP Estimator
206(7)
6.5.1 TPP Response to Noise: PfaTPP
206(3)
6.5.2 TPP Response to a Landmark and Noise: PDTPP
209(1)
6.5.3 Choice of αp, TTPP (αp, l) and l
210(1)
6.5.4 Numerical Method for Determining TTPP (αp, l) and PDTPP
211(2)
6.6 A Comparison of Scan Integration Methods
213(1)
6.7 A Note on Multi-Path Reflections
214(1)
6.8 TPP Integration of Radar in an Urban Environment
215(8)
6.8.1 Qualitative Assessment of TPP Applied to A-Scope Information
215(1)
6.8.2 Quantitative Assessment of TPP Applied to Complete Scans
215(6)
6.8.3 A Qualitative Assessment of an Entire Parcking Lot Scene
221(2)
6.9 Recursive A-Scope Noise Reduction
223(5)
6.9.1 Single A-Scope Noise Subtraction
225(2)
6.9.2 Multiple A-Scope---Complete Scan Noise Subtraction
227(1)
6.10 Summary
228(1)
6.11 Bibliographical Remarks
229(4)
References
230(3)
PART III Robotic Mapping with Known Vehicle Location
233(50)
Chapter 7 Grid-Based Robotic Mapping with Detection Likelihood Filtering
235(32)
7.1 Introduction
235(2)
7.2 The Grid-Based Robotic Mapping (GBRM) Problem
237(8)
7.2.1 GBRM Based on Range Measurements
239(2)
7.2.2 GBRM with Detection Measurements
241(2)
7.2.3 Detection versus Range Measurement Models
243(2)
7.3 Mapping with Unknown Measurement Likelihoods
245(5)
7.3.1 Data Format
245(1)
7.3.2 GBRM Algorithm Overview
246(1)
7.3.3 Constant False Alarm Rate (CFAR) Detector
247(1)
7.3.4 Map Occupancy and Detection Likelihood Estimator
247(2)
7.3.5 Incorporation of the OS-CFAR Processor
249(1)
7.4 GBRM-ML Particle Filter Implementation
250(1)
7.5 Comparisons of Detection and Spatial-Based GBRM
251(10)
7.5.1 Dataset 1: Synthetic Data, Single Landmark
251(1)
7.5.2 Dataset 2: Real Experiments in the Parking Lot Environment
252(7)
7.5.3 Dataset 3: A Campus Environment
259(2)
7.6 Summary
261(1)
7.7 Bibliographical Remarks
262(5)
References
263(4)
Chapter 8 Feature-Based Robotic Mapping with Random Finite Sets
267(16)
8.1 Introduction
267(1)
8.2 The Probability Hypothesis Density (PHD)-FBRM Filter
268(1)
8.3 PHD-FBRM Filter Implementation
269(6)
8.3.1 The FBRM New Feature Proposal Strategy
271(1)
8.3.2 Gaussian Management and State Estimation
272(2)
8.3.3 GMM-PHD-FBRM Pseudo Code
274(1)
8.4 PHD-FBRM Computational Complexity
275(1)
8.5 Analysis of the PHD-FBRM Filter
275(4)
8.6 Summary
279(1)
8.7 Bibliographical Remarks
280(3)
References
281(2)
PART IV Simultaneous Localization and Mapping
283(46)
Chapter 9 Radar-Based SLAM with Random Finite Sets
285(16)
9.1 Introduction
285(1)
9.2 SLAM with the PHD Filter
286(4)
9.2.1 The Factorized RFS-SLAM Recursion
286(1)
9.2.2 PHD Mapping---Rao-Blackwellization
287(1)
9.2.3 PHD-SLAM
288(2)
9.3 Implementing the RB-PHD-SLAM Filter
290(3)
9.3.1 PHD Mapping---Implementation
290(2)
9.3.2 The Vehicle Trajectory---Implementation
292(1)
9.3.3 Estimating the Map
292(1)
9.3.4 GMM-PHD-SLAM Pseudo Code
293(1)
9.4 RB-PHD-SLAM Computational Complexity
293(2)
9.5 Radar-Based Comparisons of RFS and Vector-Based SLAM
295(4)
9.6 Summary
299(1)
9.7 Bibliographical Remarks
299(2)
References
300(1)
Chapter 10 X-Band Radar-Based SLAM in an Off-Shore Environment
301(28)
10.1 Introduction
301(2)
10.2 The ASC and the Coastal Environment
303(2)
10.3 Marine Radar Feature Extraction
305(9)
10.3.1 Adaptive Coastal Feature Detection---OS-CFAR
306(2)
10.3.2 Image-Based Smoothing---Gaussian Filtering
308(3)
10.3.3 Image-Based Thresholding
311(1)
10.3.4 Image-Based Clustering
311(2)
10.3.5 Feature Labeling
313(1)
10.4 The Marine Based SLAM Algorithms
314(4)
10.4.1 The ASC Process Model
314(1)
10.4.2 RFS SLAM with the PHD Filter
314(3)
10.4.3 NN-EKF-SLAM Implementation
317(1)
10.4.4 Multi-Hyphothesis (MH) FastSLAM Implementation
317(1)
10.5 Comparisons of SLAM Concepts at Sea
318(6)
10.5.1 SLAM Trial 1---Comparing PHD and NN-EKF-SLAM
318(4)
10.5.2 SLAM Trial 2---Comparing RB-PHD-SLAM and MH-FastSLAM
322(2)
10.6 Summary
324(2)
10.7 Bibliographical Remarks
326(3)
References
326(3)
APPENDIX A The Navtech FMCW MMW Radar Specifications
329(2)
APPENDIX B Derivation of g(Zk|Zx-1,Xk) for the RB-PHD-SLAM Filter
331(2)
B.1 The Empty Strategy
331(1)
B.2 The Single Feature Strategy
332(1)
APPENDIX C NN-EKF and FastSLAM Feature Management
333(2)
Index 335
Martin Adams is professor in the Department of Electrical Engineering, Advanced Mining Technology Centre (AMTC) at the University of Chile. He received his D.Phil. in robotics research at the University of Oxford. Ebi Jose is a senior research and development engineer at Singapore Technologies Electronics. He holds a Ph.D. in robotics from Nanyang Technological University.
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