RD-VIO : Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. First, we propose an IMU-PARSAC algorithm w...

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Publié dans:IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 10 vom: 12. Okt., Seite 6941-6955
Auteur principal: Li, Jinyu (Auteur)
Autres auteurs: Pan, Xiaokun, Huang, Gan, Zhang, Ziyang, Wang, Nan, Bao, Hujun, Zhang, Guofeng
Format: Article en ligne
Langue:English
Publié: 2024
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article
Description
Résumé:It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. First, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Second, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets and online comparison. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments
Description:Date Revised 05.09.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2024.3353263