Cameras and inertial measurement units are complementary sensors for
ego-motion estimation and environment mapping. Their combination makes
visual-inertial odometry (VIO) systems more accurate and robust. For
globally consistent mapping, however, combining visual and inertial
information is not straightforward. To estimate the motion and geometry
with a set of images large baselines are required. Because of that,
most systems operate on keyframes that have large time intervals
between each other. Inertial data on the other hand quickly degrades
with the duration of the intervals and after several seconds of
integration, it typically contains only little useful information.
In this paper, we propose to extract relevant information for
visual-inertial mapping from visual-inertial odometry using non-linear
factor recovery. We reconstruct a set of non-linear factors that make
an optimal approximation of the information on the trajectory
accumulated by VIO. To obtain a globally consistent map we combine
these factors with loop-closing constraints using bundle adjustment.
The VIO factors make the roll and pitch angles of the global map
observable, and improve the robustness and the accuracy of the mapping.
In experiments on a public benchmark, we demonstrate superior
performance of our method over the state-of-the-art approaches.