{"product_id":"multimodal-perception-and-secure-state-estimation-for-robotic-mobility-platforms-9781119876014","title":"Multimodal Perception and Secure State Estimation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAbout the Authors xii\u003c\/p\u003e \u003cp\u003ePreface xiv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Background and Motivation 1\u003c\/p\u003e \u003cp\u003e1.2 Multimodal Pose Estimation for Vehicle Navigation 2\u003c\/p\u003e \u003cp\u003e1.2.1 Multi-Senor Pose Estimation 2\u003c\/p\u003e \u003cp\u003e1.2.2 Pose Estimation with Constraints 4\u003c\/p\u003e \u003cp\u003e1.2.3 Research Focus in Multimodal Pose Estimation 5\u003c\/p\u003e \u003cp\u003e1.3 Secure Estimation 7\u003c\/p\u003e \u003cp\u003e1.3.1 Secure State Estimation under Cyber Attacks 7\u003c\/p\u003e \u003cp\u003e1.3.2 Secure Pose Estimation for Autonomous Vehicles 8\u003c\/p\u003e \u003cp\u003e1.4 Contributions and Organization 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Multimodal Perception in Vehicle Pose Estimation \u003c\/b\u003e\u003cb\u003e13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Heading Reference-Assisted Pose Estimation \u003c\/b\u003e\u003cb\u003e15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Preliminaries 16\u003c\/p\u003e \u003cp\u003e2.1.1 Stereo Visual Odometry 16\u003c\/p\u003e \u003cp\u003e2.1.2 Heading Reference Sensors 17\u003c\/p\u003e \u003cp\u003e2.1.3 Graph Optimization on a Manifold 17\u003c\/p\u003e \u003cp\u003e2.2 Abstraction Model of Measurement with a Heading Reference 19\u003c\/p\u003e \u003cp\u003e2.2.1 Loosely Coupled Model 19\u003c\/p\u003e \u003cp\u003e2.2.2 Tightly Coupled Model 20\u003c\/p\u003e \u003cp\u003e2.2.3 Structure of the Abstraction Model 22\u003c\/p\u003e \u003cp\u003e2.2.4 Vertex Removal in the Abstraction Model 22\u003c\/p\u003e \u003cp\u003e2.3 Heading Reference-Assisted Pose Estimation (HRPE) 24\u003c\/p\u003e \u003cp\u003e2.3.1 Initialization 24\u003c\/p\u003e \u003cp\u003e2.3.2 Graph Optimization 24\u003c\/p\u003e \u003cp\u003e2.3.3 Maintenance of the Dynamic Graph 26\u003c\/p\u003e \u003cp\u003e2.4 Simulation Studies 26\u003c\/p\u003e \u003cp\u003e2.4.1 Accuracy with Respect to Heading Measurement Error 28\u003c\/p\u003e \u003cp\u003e2.4.2 Accuracy with Respect to Sliding Window Size 28\u003c\/p\u003e \u003cp\u003e2.4.3 Time Consumption with Respect to Sliding Window Size 28\u003c\/p\u003e \u003cp\u003e2.5 Experimental Results 31\u003c\/p\u003e \u003cp\u003e2.5.1 Experimental Platform 31\u003c\/p\u003e \u003cp\u003e2.5.2 Pose Estimation Performance 33\u003c\/p\u003e \u003cp\u003e2.5.3 Real-Time Performance 34\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Road-Constrained Localization Using Cloud Models \u003c\/b\u003e\u003cb\u003e37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Preliminaries 38\u003c\/p\u003e \u003cp\u003e3.1.1 Scaled Measurement Equations for Visual Odometry 38\u003c\/p\u003e \u003cp\u003e3.1.2 Cloud Models 39\u003c\/p\u003e \u003cp\u003e3.1.3 Uniform Gaussian Distribution (UGD) 39\u003c\/p\u003e \u003cp\u003e3.1.4 Gaussian-Gaussian Distribution (GGD) 42\u003c\/p\u003e \u003cp\u003e3.2 Map-Assisted Ground Vehicle Localization 43\u003c\/p\u003e \u003cp\u003e3.2.1 Measurement Representation with UGD 44\u003c\/p\u003e \u003cp\u003e3.2.2 Shape Matching Between Map and Particles 45\u003c\/p\u003e \u003cp\u003e3.2.3 Particle Resampling and Parameter Estimation 46\u003c\/p\u003e \u003cp\u003e3.2.4 Framework Extension to Other Cloud Models 47\u003c\/p\u003e \u003cp\u003e3.3 Experimental Validation on UGD 47\u003c\/p\u003e \u003cp\u003e3.3.1 Configurations 47\u003c\/p\u003e \u003cp\u003e3.3.2 Localization with Stereo Visual Odometry 48\u003c\/p\u003e \u003cp\u003e3.3.3 Localization with Monocular Visual Odometry 49\u003c\/p\u003e \u003cp\u003e3.3.4 Scale Estimation Results 52\u003c\/p\u003e \u003cp\u003e3.3.5 Weighting Function Balancing 52\u003c\/p\u003e \u003cp\u003e3.4 Experimental Validation on GGD 54\u003c\/p\u003e \u003cp\u003e3.4.1 Experiments on KITTI 55\u003c\/p\u003e \u003cp\u003e3.4.2 Experiments on the Self-Collected Dataset 61\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 GPS\/Odometry\/Map Fusion for Vehicle Positioning Using Potential Functions \u003c\/b\u003e\u003cb\u003e65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Potential Wells and Potential Trenches 66\u003c\/p\u003e \u003cp\u003e4.1.1 Potential Function Creation 67\u003c\/p\u003e \u003cp\u003e4.1.2 Minimum Searching 71\u003c\/p\u003e \u003cp\u003e4.2 Potential-Function-Based Fusion for Vehicle Positioning 74\u003c\/p\u003e \u003cp\u003e4.2.1 Information Sources and Sensors 74\u003c\/p\u003e \u003cp\u003e4.2.2 Potential Representation 76\u003c\/p\u003e \u003cp\u003e4.2.3 Road-Switching Strategy 76\u003c\/p\u003e \u003cp\u003e4.3 Experimental Results 78\u003c\/p\u003e \u003cp\u003e4.3.1 Quantitative Results 78\u003c\/p\u003e \u003cp\u003e4.3.2 Qualitative Evaluation 80\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multi-Sensor Geometric Pose Estimation \u003c\/b\u003e\u003cb\u003e85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Preliminaries 86\u003c\/p\u003e \u003cp\u003e5.1.1 Distance on Riemannian Manifolds 86\u003c\/p\u003e \u003cp\u003e5.1.2 Probabilistic Distribution on Riemannian Manifolds 87\u003c\/p\u003e \u003cp\u003e5.2 Geometric Pose Estimation Using Dynamic Potential Fields 88\u003c\/p\u003e \u003cp\u003e5.2.1 State Space and Measurement Space 88\u003c\/p\u003e \u003cp\u003e5.2.2 Dynamic Potential Fields on Manifolds 90\u003c\/p\u003e \u003cp\u003e5.2.3 DPF-Based Information Fusion 91\u003c\/p\u003e \u003cp\u003e5.2.4 Approximation of Geometric Pose Estimation 95\u003c\/p\u003e \u003cp\u003e5.3 VO-Heading-Map Pose Estimation for Ground Vehicles 97\u003c\/p\u003e \u003cp\u003e5.3.1 System Modeling 97\u003c\/p\u003e \u003cp\u003e5.3.2 Road Constraints 98\u003c\/p\u003e \u003cp\u003e5.3.3 Parameter Estimation on SE(3) 99\u003c\/p\u003e \u003cp\u003e5.4 Experiments on KITTI Sequences 99\u003c\/p\u003e \u003cp\u003e5.4.1 Overall Performance 99\u003c\/p\u003e \u003cp\u003e5.4.2 Influence of Heading Error 102\u003c\/p\u003e \u003cp\u003e5.4.3 Influence of Road Map Resolution 102\u003c\/p\u003e \u003cp\u003e5.4.4 Influences of Parameters 104\u003c\/p\u003e \u003cp\u003e5.5 Experiments on the NTU Dataset 105\u003c\/p\u003e \u003cp\u003e5.5.1 Overall Performance 105\u003c\/p\u003e \u003cp\u003e5.5.2 Phenomena Observed During Experiments 105\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Secure State Estimation for Mobile Robots \u003c\/b\u003e\u003cb\u003e109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Filter-Based Secure Dynamic Pose Estimation \u003c\/b\u003e\u003cb\u003e111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 RelatedWork 113\u003c\/p\u003e \u003cp\u003e6.3 Problem Formulation 114\u003c\/p\u003e \u003cp\u003e6.3.1 System Model 114\u003c\/p\u003e \u003cp\u003e6.3.2 Measurement Model 116\u003c\/p\u003e \u003cp\u003e6.3.3 Attack Model 116\u003c\/p\u003e \u003cp\u003e6.4 Estimator Design 117\u003c\/p\u003e \u003cp\u003e6.5 Discussion of Parameter Selection 122\u003c\/p\u003e \u003cp\u003e6.5.1 The Probability Subject to Deception Attacks 122\u003c\/p\u003e \u003cp\u003e6.5.2 The Bound of Signal \u003cb\u003e\u003ci\u003e𝝃\u003c\/i\u003e\u003c\/b\u003e\u003ci\u003e\u003csub\u003ek\u003c\/sub\u003e \u003c\/i\u003e123\u003c\/p\u003e \u003cp\u003e6.6 Experimental Validation 123\u003c\/p\u003e \u003cp\u003e6.6.1 Pose Estimation under Attack on a Single State 125\u003c\/p\u003e \u003cp\u003e6.6.2 Pose Estimation under Attacks on Multiple States 127\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks \u003c\/b\u003e\u003cb\u003e131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 131\u003c\/p\u003e \u003cp\u003e7.2 Related Work 133\u003c\/p\u003e \u003cp\u003e7.3 Pose Estimation Problem for Ground Vehicles under Attack 134\u003c\/p\u003e \u003cp\u003e7.3.1 System Model 134\u003c\/p\u003e \u003cp\u003e7.3.2 Attack Model 136\u003c\/p\u003e \u003cp\u003e7.4 Design of the Unscented Kalman Filter 137\u003c\/p\u003e \u003cp\u003e7.5 Numeric Simulation 141\u003c\/p\u003e \u003cp\u003e7.6 Experiments 144\u003c\/p\u003e \u003cp\u003e7.6.1 General Performance 145\u003c\/p\u003e \u003cp\u003e7.6.2 Influence of Parameters 145\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Secure Dynamic State Estimation with a Decomposing Kalman Filter \u003c\/b\u003e\u003cb\u003e149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 149\u003c\/p\u003e \u003cp\u003e8.2 Problem Formulation 151\u003c\/p\u003e \u003cp\u003e8.3 Decomposition of the Kalman Filter By Using a Local Estimate 153\u003c\/p\u003e \u003cp\u003e8.4 A Secure Information Fusion Scheme 158\u003c\/p\u003e \u003cp\u003e8.5 Numerical Example 161\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 162\u003c\/p\u003e \u003cp\u003e8.7 Appendix: Proof of Theorem 8.2 162\u003c\/p\u003e \u003cp\u003e8.8 Proof of Theorem 8.4 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Secure Dynamic State Estimation for AHRS \u003c\/b\u003e\u003cb\u003e169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 169\u003c\/p\u003e \u003cp\u003e9.2 Related Work 170\u003c\/p\u003e \u003cp\u003e9.2.1 Attitude Estimation 170\u003c\/p\u003e \u003cp\u003e9.2.2 Secure State Estimation 171\u003c\/p\u003e \u003cp\u003e9.2.3 Secure Attitude Estimation 171\u003c\/p\u003e \u003cp\u003e9.3 Attitude Estimation Using Heading References 172\u003c\/p\u003e \u003cp\u003e9.3.1 Attitude Estimation from Vector Observations 172\u003c\/p\u003e \u003cp\u003e9.3.2 Secure Attitude Estimation Framework and Modeling 173\u003c\/p\u003e \u003cp\u003e9.4 Secure Estimator Design with a Decomposing Kalman Filter 174\u003c\/p\u003e \u003cp\u003e9.4.1 Decomposition of the Kalman Filter Using a Local Estimate 176\u003c\/p\u003e \u003cp\u003e9.4.2 A Least-Square Interpretation for the Decomposition 177\u003c\/p\u003e \u003cp\u003e9.4.3 Secure State Estimate 178\u003c\/p\u003e \u003cp\u003e9.5 Simulation Validation 181\u003c\/p\u003e \u003cp\u003e9.5.1 Simulating Measurements with Attacks 182\u003c\/p\u003e \u003cp\u003e9.5.2 Filter Performance 182\u003c\/p\u003e \u003cp\u003e9.5.3 Influence of Parameter \u003ci\u003e𝛾 \u003c\/i\u003e182\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Conclusions \u003c\/b\u003e185\u003c\/p\u003e \u003cp\u003eReferences 189\u003c\/p\u003e \u003cp\u003eIndex 207\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407180472663,"sku":"9781119876014","price":75.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119876014.jpg?v=1730498456","url":"https:\/\/bookcurl.com\/products\/multimodal-perception-and-secure-state-estimation-for-robotic-mobility-platforms-9781119876014","provider":"Book Curl","version":"1.0","type":"link"}