Gravity-aware partially calibrated absolute pose estimation from affine- or rotation-covariant features
Inertial measurement units (IMUs) are now standard in most consumer devices, such as smartphones, drones, and extended reality (XR) headsets. By fusing visual and inertial data, localization systems gain significantly in speed and robustness compared to vision-only or IMU-only approaches. However, traditional pose estimation methods fail to utilize the local geometric information embedded in feature descriptors like SIFT. Recent work has proved the advantages of leveraging this information for relative and absolute pose estimation, but its application to partially calibrated absolute pose esti
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- LinkedLinked via arxiv author · 85%Marcus Valtonen Örnhag →
“Gravity-aware partially calibrated absolute pose estimation from affine- or rotation-covariant features”
- LinkedLinked via arxiv author · 85%Alberto Jaenal →
“Gravity-aware partially calibrated absolute pose estimation from affine- or rotation-covariant features”
- LinkedLinked via arxiv author · 85%Stefan Adalbjörnsson →
“Gravity-aware partially calibrated absolute pose estimation from affine- or rotation-covariant features”