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. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments.

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。第一阶段利用视觉和IMU测量值将路标点与新关键点进行匹配，我们从匹配结果中收集统计信息，并以此指导第二阶段的关键点内部匹配。其次，为处理纯旋转问题，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。将纯旋转帧归为特殊子帧，在求解视觉惯性光束法平差时，这些子帧为纯旋转运动提供额外约束。我们在公开数据集上评估了所提出的VIO系统。实验表明，所提出的RD-VIO在动态环境下相比其他方法具有明显优势。