Aug 22, 2023 · We propose VIO-DualProNet, a novel approach that utilizes deep learning methods to dynamically estimate the inertial noise uncertainty in real-time.
We propose VIO-DualProNet, a novel approach that utilizes deep learning methods to dynamically estimate the inertial measurement unit (IMU) noise uncertainty ...
VIO-DualProNet: An adaptive, deep-learning based, inertial noise parameter tuning algorithm, dedicated to optimization-based, factor graph, VIO algorithms.
To circumvent this, we propose VIO-DualProNet, a novel approach that utilizes deep learning methods to dynamically estimate the inertial noise uncertainty in ...
Visual-Inertial Odometry with Learning Based Process Noise Covariance
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It combines visual and inertial measurements to accurately estimate position and orientation. Existing VIO methods assume a fixed noise covariance for the ...
In this paper, we analyze the observability of the visual-inertial odometry (VIO) using stereo cameras with a velocity-control based kinematic motion model.
Aslan, HVIOnet: A deep learning based hybrid visual-inertial odometry approach for unmanned aerial system position estimation, Neural Netw., № 155, с. 461
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We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate ...
VIO is a method that helps devices like robots, self-driving cars, and augmented reality systems figure out where they are and which way they ...
Jun 3, 2024 · Dan Solodar , Itzik Klein : VIO-DualProNet: Visual-inertial odometry with learning based process noise covariance. Eng. Appl. Artif. Intell.