In motion tracking of connected multi-body systems Inertial Measurement Units (IMUs) are used in a wide variety of applications, since they provide a low-cost easy-to-use method for orientation estimation. However, in indoor environments or near ferromagnetic material the magnetic field is inhomogeneous which limits the accuracy of tracking algorithms using magnetometers. Methods that use only accelerometers and gyroscopes on the other hand yield no information on the absolute heading of the tracked object. For objects connected by rotational joints with range of motion constraints we propose a method that provides a magnetometer-free, long-term stable relative orientation estimate based on a non-linear, window-based cost function. The method can be used for real-time estimation as well as post-processing. It is validated experimentally with a mechanical joint and compared to other methods that are used in motion tracking. It is shown that for the used test object, the proposed methods yields the best results with a total angle error of less than 4 degrees for all experiments.
Synchronous Observer Design for Landmark-Inertial SLAM with Magnetometer and Intermittent GNSS Measurements
Centre for Systems and Control, Indian Institute of Technology Bombay（印度理工学院孟买分校系统与控制中心）；School of Aerospace, Mechanical and Mechatronic Engineering (AMME), Faculty of Engineering, University of Sydney（悉尼大学工程学院航空航天、机械与机电工程系）
Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic actuators. A major challenge is the presence of magnetic disturbances that result in unreliable magnetometer readings. Recent research revealed that this problem can be overcome by exploitation of kinematic constraints. While typically each segment of the kinematic chain is equipped with an IMU, a novel approach called sparse inertial motion tracking aims at infering the complete motion states from measurements of a…
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU
Department of Engineering ``Enzo Ferrari'', University of Modena（工程系（恩佐·费拉里），莫德纳大学）；Department of Science and Methods for Engineering, University of Modena（工程科学与方法系，莫德纳大学）
MinJointTracker: Real-time inertial kinematic chain tracking with joint position estimation and minimal state size
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU)（莱茵-瓦尔兹技术大学凯泽斯劳滕-兰道）；IT & Engineering, IU Internationale Hochschule Erfurt（IT与工程，国际高等学院埃弗特）
Delft University of Technology（代尔夫特理工大学）；Technische Universität Berlin（柏林工业大学）；KU Leuven（鲁汶大学）
Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain can be tracked accurately. These methods specifically avoid using magnetometer measurements, which are known to be unreliable since the magnetic field at the different sensor locations is typically different. They rely on the assumption that the motion of th…
Cascaded Tightly-Coupled Observer Design for Single-Range-Aided Inertial Navigation
Department of Computer Science（计算机科学系）；Engineering, University of Quebec in Outaouais, Gatineau, QC, Canada（魁北克大学Outaouais工程学院，加蒂纳岛，QC，加拿大）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Lakehead University（拉克头大学）
Delft Center for Systems and Control, Delft University of Technology（代尔夫特理工大学代尔夫特系统与控制中心）；Department of Information Technology, Uppsala University（乌普萨拉大学信息技术系）
We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g. caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional upda…
Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
Robotics, Intelligent Systems, and Control Lab, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia（机器人、智能系统与控制实验室，国王阿卜杜勒-阿齐兹大学科学与技术学院（KAUST），沙特阿拉伯图瓦尔）
Nanyang Technological University（南洋理工大学）；The Chinese University of Hong Kong（香港中文大学）
Tracking interpersonal distances is essential for real-time social distancing management and {\em ex-post} contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo …
Autonomous missions of drones require continuous and reliable estimates for the drone's attitude, velocity, and position. Traditionally, these states are estimated by applying Extended Kalman Filter (EKF) to Accelerometer, Gyroscope, Barometer, Magnetometer, and GPS measurements. When the GPS signal is lost, position and velocity estimates deteriorate quickly, especially when using low-cost inertial sensors. This paper proposes an estimation method that uses a Recurrent Neural Network (RNN) to allow reliable estimation of a drone's position and velocity in the absence of GPS signal. The RNN is…
