RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in
Dynamic Environments
- URL: http://arxiv.org/abs/2310.15072v3
- Date: Fri, 16 Feb 2024 08:49:55 GMT
- Title: RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in
Dynamic Environments
- Authors: Jinyu Li, Xiaokun Pan, Gan Huang, Ziyang Zhang, Nan Wang, Hujun Bao,
Guofeng Zhang
- Abstract summary: It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation.
We design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these problems.
- Score: 55.864869961717424
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. Firstly, 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. Secondly, 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. The source code is
available at:
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