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