Robotic navigation and mapping with radar

Focusing on autonomous robotic applications, this resource offers a practical treatment of short-range radar processing for reliable object detection at the ground level. It demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles.

Author Notes

Martin Adams is a professor in the department of electrical engineering and a member of the Advanced Mining Technology Centre (AMTC) at the University of Chile. He received his D.Phil. in engineering science at the University of Oxford.
John Mullane is a research scientist at Projective Space Pte. Ltd., Singapore. He holds a Ph.D. in electrical and electronic engineering from Nanyang Technological University.
Ehi Jose is a research scientist at Projective Space Pte. Ltd., Singapore. He holds a Ph.D. in electrical and electronic engineering from Nanyang Technological University.
Ba-Ngu Vo is a professor at the School of Electrical, Electronic and Computer Engineering at the University of Western Australia. He received his Ph.D. in engineering from Curtin University.

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

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