scholarly journals Motion tracking algorithms for inertial measurement

2007 ◽  
Author(s):  
Javier Torres ◽  
Brendan O’Flynn ◽  
Philip Angove ◽  
Frank Murphy ◽  
Cian O’ Mathuna
Keyword(s):  
Motion Tracking ◽  
Inertial Measurement ◽  
Tracking Algorithms

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.

Towards Benchmarking of Video Motion Tracking Algorithms

2010 ◽  
Author(s):  
Mukesh Motwani ◽  
Nishith Tirpankar ◽  
Rakhi Motwani ◽  
Monica Nicolescu ◽  
Frederick C. Harris Jr.
Keyword(s):  
Motion Tracking ◽  
Tracking Algorithms

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.

Sign in / Sign up

Export Citation Format

Share Document