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: \href{https://github.com/openxrlab/xrslam}{{\fontfamily{pcr}\selectfont https://github.com/openxrlab/xrslam}}.

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