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}}.

翻译：视觉或视觉-惯性里程计系统在处理动态场景和纯旋转问题时通常面临挑战。本文设计了一种名为RD-VIO的新型视觉-惯性里程计（VIO）系统，以同时应对这两个问题。首先，我们提出了一种IMU-PARSAC算法，该算法能够通过两阶段过程鲁棒地检测和匹配关键点。在第一阶段，利用视觉和IMU测量值将地标与新关键点进行匹配，随后从匹配中收集统计信息，并在第二阶段指导关键点内匹配。其次，为解决纯旋转问题，我们检测运动类型并在数据关联过程中采用延迟三角化技术。将纯旋转帧归入特殊子帧，在求解视觉-惯性束调整时，它们为纯旋转运动提供额外约束。我们在公开数据集和在线对比中评估了所提出的VIO系统。实验表明，在动态环境下，本文提出的RD-VIO相比其他方法具有明显优势。源代码已开源：\href{https://github.com/openxrlab/xrslam}{{\fontfamily{pcr}\selectfont https://github.com/openxrlab/xrslam}}