scholarly journals Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing Under Optimal Conditions: One Size Does Not Fit All

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2543
Author(s):  
Marco Caruso ◽  
Angelo Maria Sabatini ◽  
Daniel Laidig ◽  
Thomas Seel ◽  
Marco Knaflitz ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Recent Literature ◽  
Human Motion ◽  
Measurement Unit ◽  
Optimal Conditions ◽  
Inertial Measurement ◽  
Rotation Rates ◽  
Significant Performance ◽  
Parameter Values

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.

scholarly journals Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 142 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Xinghang Zhou ◽  
Shihong Duan
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Human Motion ◽  
Measurement Unit ◽  
Time Of Arrival ◽  
Fusion Method ◽  
Tracking Accuracy ◽  
Fundamental Limits ◽  
Inertial Measurement ◽  
Human Motion Tracking

Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

scholarly journals Robocentric visual–inertial odometry

2019 ◽  
pp. 027836491985336 ◽  
Author(s):  
Zheng Huai ◽  
Guoquan Huang
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Inertial Navigation System ◽  
State Of The Art ◽  
Sliding Window ◽  
Frame Of Reference ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Sensing Platforms ◽  
The World

In this paper, we propose a novel robocentric formulation of the visual–inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual–inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a six-axis inertial measurement unit (IMU). The key idea is to deliberately reformulate the VINS with respect to a moving local frame, rather than a fixed global frame of reference as in the standard world-centric VINS, in order to obtain relative motion estimates of higher accuracy for updating global pose. As an immediate advantage of this robocentric formulation, the proposed R-VIO can start from an arbitrary pose, without the need to align the initial orientation with the global gravitational direction. More importantly, we analytically show that the linearized robocentric VINS does not undergo the observability mismatch issue as in the standard world-centric counterparts that has been identified in the literature as the main cause of estimation inconsistency. Furthermore, we investigate in depth the special motions that degrade the performance in the world-centric formulation and show that such degenerate cases can be easily compensated for by the proposed robocentric formulation, without resorting to additional sensors as in the world-centric formulation, thus leading to better robustness. The proposed R-VIO algorithm has been extensively validated through both Monte Carlo simulation and real-world experiments with different sensing platforms navigating in different environments, and shown to achieve better (or competitive at least) performance than the state-of-the-art VINS, in terms of consistency, accuracy, and efficiency.

The Application of Inertial Measurement Unit in Inertia Parameter Identification

2011 ◽  
Vol 201-203 ◽  
pp. 974-981 ◽  
Author(s):  
Bo Wang ◽  
Zhong Xi Hou ◽  
Xian Zhong Gao ◽  
Shang Qiu Shan
Keyword(s):  
Angular Velocity ◽  
Parameter Identification ◽  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Oscillation Period ◽  
Experimental Manipulation ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Periodic Movement ◽  
Inertia Parameter

This paper presents a simple but effective method for inertial parameter identification with symmetrical trifilar pendulum and inertial measurement unit (IMU). An improvement upon conventional pendulum method is described by introducing IMU to identify the orientation of specimen by self-alignment, and then complicated equipment and experimental manipulation are not needed any more. Based on the excellent capacity of motion tracking, the IMU is also used for recording the characteristic of periodic movement by collected the information about acceleration and angular velocity. The main sources of identification errors are discussed from a set of examples. Then an experiment is carried out, and Fourier analysis is used to gain the oscillation period, which made the measurement much more convenient and accurate. The identification results are also presented by comparing with reference values computed from geometrical considerations, which proves the effectiveness of such method.

Tracking Using an Inertial Measurement Unit (IMU) and GPS

2012 ◽  
Vol 229-231 ◽  
pp. 1469-1475 ◽  
Author(s):  
Hussein M. Magboub ◽  
Mohamed A. Msallem ◽  
Nasser Ali
Keyword(s):  
Inertial Measurement Unit ◽  
Simulation Software ◽  
Measurement Unit ◽  
Matlab Simulation ◽  
Tracking Accuracy ◽  
Inertial Measurement ◽  
Wide Range ◽  
Different Types ◽  
Significant Performance ◽  
Significant Attention

Since the last decade, vehicle tracking has been attracting significant attention in a wide range of applications. To deliver on their requirements, these applications need a specific tracking accuracy. However, current tracking techniques lack the required accuracy, especially for mission critical applications. Although these techniques have demonstrated significant performance improvement, there remain situations that give rise to degraded tracking accuracy, a deficiency that many applications cannot tolerate. This has motivated the research and development of advanced tracking. In this paper will be the design and implementation of an inertial navigation system (INS) using an inertial measurement unit (IMU) and GPS by Matlab simulation software. The INS is capable of providing continuous estimates of a vehicle’s position and orientation. And Comparative study of different types of estimation filters (KF, EKF) which has high accuracy is used to improve system state estimation.

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