The delayed-state Kalman filter yields identical updates to stochastic cloning for correlated delayed-state measurements without state augmentation.
Visual-Inertial Odometry of Aerial Robots
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Visual-Inertial odometry (VIO) is the process of estimating the state (pose and velocity) of an agent (e.g., an aerial robot) by using only the input of one or more cameras plus one or more Inertial Measurement Units (IMUs) attached to it. VIO is the only viable alternative to GPS and lidar-based odometry to achieve accurate state estimation. Since both cameras and IMUs are very cheap, these sensor types are ubiquitous in all today's aerial robots.
years
2025 2verdicts
UNVERDICTED 2representative citing papers
The paper presents a 5G terrestrial positioning system using multi-carrier carrier phase ranging, deep learning for NLOS identification, and IMU/camera sensor fusion via error-state EKF, achieving less than 5 meters error on simulated 5G signals over KITTI urban trajectories.
citing papers explorer
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Remarks on stochastic cloning and delayed-state filtering
The delayed-state Kalman filter yields identical updates to stochastic cloning for correlated delayed-state measurements without state augmentation.
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A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios
The paper presents a 5G terrestrial positioning system using multi-carrier carrier phase ranging, deep learning for NLOS identification, and IMU/camera sensor fusion via error-state EKF, achieving less than 5 meters error on simulated 5G signals over KITTI urban trajectories.