For solving the problem of polar performance of the inertial navigation system (INS) at mid-low latitudes, the simulation test system constructed by the "attitude and velocity invariant method of trajectory transfer rule based on the transverse coordinate system (T-AVIM)" of the Earth sphere model is used. The test system structure, especially the IMU conversion formula from mid-low latitudes to polar region simulation test, is introduced, and it is proved that the IMU conversion error can be equivalently superimposed on the bias error of the polar simulated IMU. According to the marine estimation formula for the effect of the reference error on the IMU conversion error, the specific influence of the constant error component and the random error component of the reference system on the simulated IMU is analyzed. The calculation method of the simulated IMU error is given with examples and intuitively explained, and the correctness of the theory is verified through simulation experiments.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412435 | PMC |
| http://dx.doi.org/10.3390/s22165988 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Context-Aware Integrated Navigation System Based on Deep Learning for Seamless Localization.
Sensors (Basel)
November 2024
Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea.
An integrated navigation system is a promising solution to improve positioning performance by complementing estimated positioning in each sensor, such as a global positioning system (GPS), an inertial measurement unit (IMU), and an odometer sensor. However, under GPS-disabled environments, such as urban canyons or tunnels where the GPS signals are difficult to receive, the positioning performance of the integrated navigation system decreases. Therefore, deep learning-based integrated navigation systems have been proposed to ensure seamless localization under various positioning conditions.
View Article and Find Full Text PDFIMU-aided adaptive mesh-grid based video motion deblurring.
PeerJ Comput Sci
November 2024
Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. In this study, we propose a novel algorithm for motion deblurring that utilizes an adaptive mesh-grid approach to manage non-uniform motion blur with a focus on reducing the computational cost.
View Article and Find Full Text PDFA Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping.
Sensors (Basel)
November 2024
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.
Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping).
View Article and Find Full Text PDFResearch on Visual-Inertial Measurement Unit Fusion Simultaneous Localization and Mapping Algorithm for Complex Terrain in Open-Pit Mines.
Sensors (Basel)
November 2024
School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China.
As mining technology advances, intelligent robots in open-pit mining require precise localization and digital maps. Nonetheless, significant pitch variations, uneven highways, and rocky surfaces with minimal texture present substantial challenges to the precision of feature extraction and positioning in traditional visual SLAM systems, owing to the intricate terrain features of open-pit mines. This study proposes an improved SLAM technique that integrates visual and Inertial Measurement Unit (IMU) data to address these challenges.
View Article and Find Full Text PDFAssessing Total Knee Arthroplasty Implant Balance with a Passive Knee Drop Test.
Surg Technol Int
November 2024
Stryker, Joint Replacement, Mahwah, New Jeresey.
Article Synopsis
- The study focuses on soft-tissue balancing in total knee arthroplasty (TKA), emphasizing a new method called the modified pendulum knee drop (PKD) test to quantitatively assess knee stiffness rather than relying on subjective observations.
- Eleven cadaver specimens were used in robotic-assisted TKA procedures, with some receiving cruciate-retaining and others posterior-stabilized implants, while the PKD test was applied to analyze the stiffness changes due to varying insert thicknesses.
- An inertial measurement unit (IMU) sensor recorded the knee's range of motion and oscillation characteristics to calculate stiffness, with results averaged over multiple trials for each insert thickness tested.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!