Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms

Description

Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms Enables readers to understand important new trends in multimodal perception for mobile robotics This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. It thoroughly evaluates filter-based secure dynamic pose estimation approaches for autonomous vehicles over multiple attack signals and shows that they outperform conventional Kalman filtered results. As a modern learning resource, it contains extensive simulative and experimental results that have been successfully implemented on various models and real platforms. To aid in reader comprehension, detailed and illustrative examples on algorithm implementation and performance evaluation are also presented. Written by four qualified authors in the field, sample topics covered in the book include: Secure state estimation that focuses on system robustness under cyber-attacksMulti-sensor fusion that helps improve system performance based on the complementary characteristics of different sensorsA geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, which has been validated using public and self-collected dataHow to achieve real-time road-constrained and heading-assisted pose estimation This book will appeal to graduate-level students and professionals in the fields of ground vehicle pose estimation and perception who are looking for modern and updated insight into key concepts related to the field of robotic mobility platforms. Cover Title Page Copyright Contents About the Authors Preface Chapter 1 Introduction 1.1 Background and Motivation 1.2 Multimodal Pose Estimation for Vehicle Navigation 1.2.1 Multi‐Senor Pose Estimation 1.2.2 Pose Estimation with Constraints 1.2.3 Research Focus in Multimodal Pose Estimation 1.3 Secure Estimation 1.3.1 Secure State Estimation under Cyber Attacks 1.3.2 Secure Pose Estimation for Autonomous Vehicles 1.4 Contributions and Organization Part I Multimodal Perception in Vehicle Pose Estimation Chapter 2 Heading Reference‐Assisted Pose Estimation 2.1 Preliminaries 2.1.1 Stereo Visual Odometry 2.1.2 Heading Reference Sensors 2.1.3 Graph Optimization on a Manifold 2.2 Abstraction Model of Measurement with a Heading Reference 2.2.1 Loosely Coupled Model 2.2.2 Tightly Coupled Model 2.2.3 Structure of the Abstraction Model 2.2.4 Vertex Removal in the Abstraction Model 2.3 Heading Reference‐Assisted Pose Estimation (HRPE) 2.3.1 Initialization 2.3.2 Graph Optimization 2.3.3 Maintenance of the Dynamic Graph 2.4 Simulation Studies 2.4.1 Accuracy with Respect to Heading Measurement Error 2.4.2 Accuracy with Respect to Sliding Window Size 2.4.3 Time Consumption with Respect to Sliding Window Size 2.5 Experimental Results 2.5.1 Experimental Platform 2.5.2 Pose Estimation Performance 2.5.3 Real‐Time Performance 2.6 Conclusion Chapter 3 Road‐Constrained Localization Using Cloud Models 3.1 Preliminaries 3.1.1 Scaled Measurement Equations for Visual Odometry 3.1.2 Cloud Models 3.1.3 Uniform Gaussian Distribution (UGD) 3.1.4 Gaussian‐Gaussian Distribution (GGD) 3.2 Map‐Assisted Ground Vehicle Localization 3.2.1 Measurement Representation with UGD 3.2.2 Shape Matching Between Map and Particles 3.2.3 Particle Resampling and Parameter Estimation 3.2.4 Framework Extension to Other Cloud Models 3.3 Experimental Validation on UGD 3.3.1 Configurations 3.3.2 Localization with Stereo Visual Odometry 3.3.3 Localization with Monocular Visual Odometry 3.3.4 Scale Estimation Results 3.3.5 Weighting Function Balancing 3.4 Experimental Validation on GGD 3.4.1 Experiments on KITTI 3.4.2 Experiments on the Self‐Collected Dataset 3.5 Conclusion Chapter 4 GPS/Odometry/Map Fusion for Vehicle Positioning Using Potential Functions 4.1 Potential Wells and Potential Trenches 4.1.1 Potential Function Creation 4.1.2 Minimum Searching 4.2 Potential‐Function‐Based Fusion for Vehicle Positioning 4.2.1 Information Sources and Sensors 4.2.2 Potential Representation 4.2.3 Road‐Switching Strategy 4.3 Experimental Results 4.3.1 Quantitative Results 4.3.2 Qualitative Evaluation 4.4 Conclusion Chapter 5 Multi‐Sensor Geometric Pose Estimation 5.1 Preliminaries 5.1.1 Distance on Riemannian Manifolds 5.1.2 Probabilistic Distribution on Riemannian Manifolds 5.2 Geometric Pose Estimation Using Dynamic Potential Fields 5.2.1 State Space and Measurement Space 5.2.2 Dynamic Potential Fields on Manifolds 5.2.3 DPF‐Based Information Fusion 5.2.4 Approximation of Geometric Pose Estimation 5.3 VO‐Heading‐Map Pose Estimation for Ground Vehicles 5.3.1 System Modeling 5.3.2 Road Constraints 5.3.3 Parameter Estimation on SE(3) 5.4 Experiments on KITTI Sequences 5.4.1 Overall Performance 5.4.2 Influence of Heading Error 5.4.3 Influence of Road Map Resolution 5.4.4 Influences of Parameters 5.5 Experiments on the NTU Dataset 5.5.1 Overall Performance 5.5.2 Phenomena Observed During Experiments 5.6 Conclusion Part II Secure State Estimation for Mobile Robots Chapter 6 Filter‐Based Secure Dynamic Pose Estimation 6.1 Introduction 6.2 Related Work 6.3 Problem Formulation 6.3.1 System Model 6.3.2 Measurement Model 6.3.3 Attack Model 6.4 Estimator Design 6.5 Discussion of Parameter Selection 6.5.1 The Probability Subject to Deception Attacks 6.5.2 The Bound of Signal ξk 6.6 Experimental Validation 6.6.1 Pose Estimation under Attack on a Single State 6.6.2 Pose Estimation under Attacks on Multiple States 6.7 Conclusion Chapter 7 UKF‐Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks 7.1 Introduction 7.2 Related Work 7.3 Pose Estimation Problem for Ground Vehicles under Attack 7.3.1 System Model 7.3.2 Attack Model 7.4 Design of the Unscented Kalman Filter 7.5 Numeric Simulation 7.6 Experiments 7.6.1 General Performance 7.6.2 Influence of Parameters 7.7 Conclusion Chapter 8 Secure Dynamic State Estimation with a Decomposing Kalman Filter 8.1 Introduction 8.2 Problem Formulation 8.3 Decomposition of the Kalman Filter By Using a Local Estimate 8.4 A Secure Information Fusion Scheme 8.5 Numerical Example 8.6 Conclusion 8.7 Appendix: Proof of Theorem 8.2 8.8 Proof of Theorem 8.4 Chapter 9 Secure Dynamic State Estimation for AHRS 9.1 Introduction 9.2 Related Work 9.2.1 Attitude Estimation 9.2.2 Secure State Estimation 9.2.3 Secure Attitude Estimation 9.3 Attitude Estimation Using Heading References 9.3.1 Attitude Estimation from Vector Observations 9.3.2 Secure Attitude Estimation Framework and Modeling 9.4 Secure Estimator Design with a Decomposing Kalman Filter 9.4.1 Decomposition of the Kalman Filter Using a Local Estimate 9.4.2 A Least‐Square Interpretation for the Decomposition 9.4.3 Secure State Estimate 9.5 Simulation Validation 9.5.1 Simulating Measurements with Attacks 9.5.2 Filter Performance 9.5.3 Influence of Parameter γ 9.6 Conclusion Chapter 10 Conclusions References Index EULA