Visual-Inertial Navigation: A Concise Review
By Guoquan Huang. IEEE International Conference on Robotics and Automation, 2019
363 citations recorded by the source corpus.
As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work – which is unfortunately missing in the literature while being greatly demanded by researchers and engineers – in the hope to accelerate the VINS research and beyond in our society as a whole.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/71b726bdbfdd256a81b80999f6a5a131af5a597e
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In Aerospace Engineering
In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
Citations in the corpus
- OpenVINS: A Research Platform for Visual-Inertial Estimation (2020, methodology): “…contextual locations within local environments, which is desired in many different applications from mobile AR/VR to autonomous navigation, visual-inertial navigation systems (VINS) are often used to provide accurate motion estimates by fusing the data from on-board camera and inertial sensors [1].”