Multisensor Attitude Estimation: Fundamental Concepts and Applications

Features

• Resembles a set of new theoretical algorithms (Kalman Filters, observers, complementary filters, etc.) on multisensor fusion in attitude estimation to help the reader to explore recent advancements.
• Case studies included on: Movea, STMicroelectronics, Xsens Technologies, Delta Drone, CTS Corporation, Texas Instruments, and LG and Samsung.
• Helps the reader to understand the basics of data fusion in attitude estimation, mathematical representations of attitude and main used sensors.
• Allows the reader to discover a set of new applications where multisensor attitude estimation find a strong interest.
• Provides the reader with a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task exploring the design of algorithms and architectures, benefits, and challenging aspects, as well as potential broad array of disciplines including navigation, robotics, biomedicine, motion analysis.

Summary

There has been an increasing interest in multi-disciplinary research on multisensor attitude estimation technology driven by its versatility and diverse areas of application, such as sensor networks, robotics, navigation, video, biomedicine, etc. Attitude estimation consists of the determination of rigid bodies’ orientation in 3D space. This research area is a multilevel, multifaceted process handling the automatic association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated by several issues and problems, such as data imperfection, data multi-modality, data dimensionality, processing framework, etc. While many of these problems have been identified and heavily investigated, no single data fusion algorithm is capable of addressing all the aforementioned challenges. The variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. Historically, the problem of attitude estimation has been introduced by Grace Wahba in 1965 within the estimate of satellite attitude and aerospace applications.

This book intends to provide the reader with both a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task. It explores the design of algorithms and architectures, benefits, and challenging aspects, as well as a broad array of disciplines, including: navigation, robotics, biomedicine, motion analysis, etc. A number of issues that make data fusion for attitude estimation a challenging task, and which will be discussed through the different chapters of the book, are related to: 1) The nature of sensors and information sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer, etc.); 2) The computational ability at the sensors; 3) The theoretical developments and convergence proofs; 4) The system architecture, computational resources, fusion level.

Table of Contents

Contents

Preface

Editors

Contributors

Historical Note

Chapter 1 What Are Quaternions and Why Haven’t I Heard of Them?

Chapter 2 Rotation in 3D Space

Chapter 3 Attitude Parametrization, Kinematics, and Dynamics

Chapter 4 Stable Estimation of Rigid Body Motion Based on the Lagrange–d’Alembert

Principle

Chapter 5 The Additive and Multiplicative Approaches to Quaternion Kalman Filtering

Chapter 6 Spacecraft Attitude Determination

Chapter 7 How to Deal with the External Acceleration When Estimating the

Attitude Using Inertial Measurement Units: A Linear Kalman-Based

Filtering Approach

Chapter 8 From Attitude Estimation to Pose Estimation Using Dual Quaternions

Chapter 9 Distributed Estimation for Spatial Rigid Motion Based on Dual Quaternions

Chapter 10 A Quaternion Orientation from Earth Field Observations Using the Algebraic

Quaternion Algorithm: Analysis and Applications in Fusion Algorithms

Chapter 11 Recent Nonlinear Attitude Estimation Algorithms

Chapter 12 Low Complexity Sensor Fusion Solution for Accurate Estimation of Gravity

and Linear Acceleration

Chapter 13 Deterministic Attitude Estimation

Chapter 14 Attitude Estimations with Intermittent Observations

Chapter 15 Estimation of Attitude from a Single-Direction Sensor

Chapter 16 Cooperative Attitude Estimation Based on Remote-Access Observations

Chapter 17 Nonlinear Observer for Attitude, Position, and Velocity: Theory

and Experiments

Chapter 18 Spacecraft Attitude Estimation Using Sparse Grid Quadrature Filtering

Chapter 19 Attitude Estimation for Small, Low-Cost UAVs: A Tutorial Approach

Chapter 20 3D Orientation Estimation Using Wearable MEMS Inertial/Magnetic Sensors

Chapter 21 Adaptive Data Fusion of Multiple Sensors for Vehicle Pose Estimation

Chapter 22 Optimal Invariant Observers Theory for Nonlinear State Estimation

Chapter 23 Design and Implementation of Low-Cost Attitude Heading References Systems

for Micro Aerial Vehicles

Chapter 24 Small Satellite Attitude Determination

Chapter 25 A Hybrid Data Fusion Approach for Robust Attitude Estimation

Chapter 26 Integration of Single-Frame and Filtering Methods for Nanosatellite Attitude

Estimation

Chapter 27 Ego-Motion Tracking in Dynamic Environments Using Wearable

Visual-Inertial Sensors

Chapter 28 Attitude Estimation for a Small-Scale Flybarless Helicopter

Chapter 29 A Comparison of Multisensor Attitude Estimation Algorithms

Chapter 30 Low-Cost and Accurate Reconstruction of Postures via IMU

Chapter 31 Attitude Estimation of a UAV Using Optical Flow

Index

Editor(s) Bio

Hassen Fourati, PhD, is currently an associate professor of the electrical engineering and computer science at the University of Grenoble Alpes, Grenoble, France, and a member of the Networked Controlled Systems Team (NeCS), affiliated to the Automatic Control Department of the GIPSA-Lab. He earned his Bachelor of Engineering degree in Electrical Engineering at the National Engineering School of Sfax, Tunisia in 2006; his Master's degree in Automated Systems and Control at the University of Claude Bernard, Lyon, France in 2007; and his PhD degree in Automatic Control at the University of Strasbourg, France in 2010. His research interests include nonlinear filtering and estimation and multisensor fusion with applications in navigation, robotics, and traffic management. Dr. Fourati has published several research papers in scientific journals, international conferences, and book chapters. He can be reached at hassen.fourati@gipsa-lab.fr.

Djamel Eddine Chouaib Belkhiat, PhD, is an associate professor in the department of physics at the Ferhat Abbas University, Setif 1, Algeria where he is also a member of Intelligent Systems Laboratory. He received the Bachelor of Engineering degree in Electrical Engineering from the Ferhat Abbas University, Setif 1, Algeria in 2007, the Master degree in Automatic Control from the University of Poitiers, France in 2008, and the Ph.D. degree in Automatic Control from the University of Reims Champagne Ardenne, France in 2011. His research interests lie in the area of monitoring and diagnosis of Hybrid Systems. Dr. Belkhiat has published several research papers in scientific journals, international conferences and book chapters. He can be reached at djamel.belkhiat@yahoo.fr.