Visual-Inertial Odometry
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Related Articles from SNS
Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems
arXiv:2606.09477v1 Announce Type: new Abstract: Estimating the relative poses of multi-camera systems is a fundamental problem in computer vision, with critical applications in autonomous vehicles, mobile devices, and unmanned aerial vehicles (UAVs). However, existing solutions often suffer from high computational complexity or rely on an excessive number of point correspondences, limiting their real-world applicability. To address these limitations, we propose two efficient minimal solvers...
Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation
Announce Type: new Abstract: This work introduces a hybrid deep learning approach integrated with an Unscented Kalman Filter (UKF) to enhance pose estimation accuracy in Visual-Inertial Odometry (VIO) for autonomous navigation. The proposed model employs a Vision Transformer (ViT) network to effectively capture temporal dependencies from inertial measurement unit (IMU) data and utilizes a Multiscale Convolutional Neural Network (MCNN) to learn optical flow-based motion cues from visual data....
Ask HN: Are you still using a Vision Pro?
Almost two years ago there was a thread on this (https://news.ycombinator.com/item?id=40872102). I'm curious now that more time has passed what people think? I use it every day, approx ~95% of the days since it launched over 2 years ago.