Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion

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

A Probabilistic Moving Horizon Estimation Framework Applied to the Visual-Inertial Sensor Fusion Problem*

2020 ◽  
Author(s):  
Felix Fiedler ◽  
Dirk Baumbach ◽  
Anko Borner ◽  
Sergio Lucia
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Moving Horizon Estimation

Vision and Inertial Sensor Fusion for Terrain Relative Navigation

2021 ◽  
Author(s):  
Andrew Verras ◽  
Roshan Thomas Eapen ◽  
Andrew B. Simon ◽  
Manoranjan Majji ◽  
Ramchander Rao Bhaskara ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Relative Navigation

scholarly journals Displacement Estimation Based on Optical and Inertial Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
External Disturbance ◽  
Linear Displacement ◽  
Measuring System ◽  
Displacement Estimation ◽  
Mems Accelerometer ◽  
Pedestrian Navigation ◽  
Complementary Filter ◽  
Optical Flow Sensor

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.

Advanced tracking through efficient image processing and visual–inertial sensor fusion

2009 ◽  
Vol 33 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Gabriele Bleser ◽  
Didier Stricker
Keyword(s):  
Image Processing ◽  
Sensor Fusion ◽  
Inertial Sensor

Exploring Inertial Sensor Fusion Methods for Direct Ergonomic Assessments

2021 ◽  
pp. 289-303
Author(s):  
Sara Santos ◽  
Duarte Folgado ◽  
João Rodrigues ◽  
Nafiseh Mollaei ◽  
Carlos Fujão ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Fusion Methods

Sensor Glove Based on Novel Inertial Sensor Fusion Control Algorithm for 3-D Real-Time Hand Gestures Measurements

2020 ◽  
Vol 67 (1) ◽  
pp. 658-666 ◽  
Author(s):  
Hsien-Ting Chang ◽  
Jen-Yuan Chang
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Control Algorithm ◽  
Inertial Sensor ◽  
Hand Gestures

scholarly journals Multi-Sensor Fusion Approach for Improving Map-Based Indoor Pedestrian Localization

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3786 ◽  
Author(s):  
Huang ◽  
Hsieh ◽  
Liu ◽  
Cheng ◽  
Hsu ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Navigation System ◽  
Inertial Sensor ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Interior Space ◽  
Localization Accuracy ◽  
Light Sensor ◽  
Indoor Navigation System ◽  
Fusion Approach

The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to the advantage of being independent of infrastructure. To improve the accuracy of the localization system, combining different technologies is one of the solutions. In this study, a multi-sensor fusion approach is proposed to improve the accuracy of the PDR system by utilizing a light sensor, Bluetooth and map information. These simple mechanisms are applied to deal with the issue of accumulative error by identifying edge and sub-edge information from both Bluetooth and the light sensor. Overall, the accumulative error of the proposed multi-sensor fusion approach is below 65 cm in different cases of light arrangement. Compared to inertial sensor-based PDR system, the proposed multi-sensor fusion approach can improve 90% of the localization accuracy in an environment with an appropriate density of ceiling-mounted lamps. The results demonstrate that the proposed approach can improve the localization accuracy by utilizing multi-sensor data and fulfill the feasibility requirements of localization in an indoor navigation system.

scholarly journals Corrigendum

2017 ◽  
Vol 36 (13-14) ◽  
pp. 1619-1619
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Full Column Rank ◽  
Rank Condition ◽  
Self Calibration ◽  
Tests Of Significance

Kelly, J. and Sukhatme, G. S. (2011). Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration, The International Journal of Robotics Research 2011: 30 (1): 56–79. DOI: http://doi.org/10.1177/0278364910382802 . In the above referenced article, on page 61, the word ‘generically’ should be omitted and the text should read: “If O has full column rank for all x ∊ M, then S satisfies the observability rank condition and is locally weakly observable (Isidori 1995).” The error does not create substantive differences in tests of significance or interpretation of the observed pattern of results.

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