ESTIMATION OF INERTIAL SENSOR TO CAMERA ROTATION FROM SINGLE AXIS MOTION

2020 ◽  
Vol 10 (5) ◽  
pp. 689-691
Author(s):  
Jaime Viera‐Artiles ◽  
José J. Valdiande ◽  
Javier Ospina ◽  
María Costales ◽  
José M. López‐Higuera

2021 ◽  
Author(s):  
Andrew Verras ◽  
Roshan Thomas Eapen ◽  
Andrew B. Simon ◽  
Manoranjan Majji ◽  
Ramchander Rao Bhaskara ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.


2021 ◽  
Vol 6 (2) ◽  
pp. 819-826
Author(s):  
Youngji Kim ◽  
Sungho Yoon ◽  
Sujung Kim ◽  
Ayoung Kim

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