中国科学院自动化研究所机构知识库

Abstract—Man-made buildings exhibit structural regularity,
which can provide strongly geometrical constraints for Visual-
Inertial Odometry (VIO) systems. To make full use of the structural
information, we propose a new structural regularity aided
VIO with novel coordinate alignment and line triangulation under
Manhattan world assumption. The proposed VIO system is built
upon OpenVINS [1] and partly based on our previous work [2].
The proposed coordinate alignment method makes the Jacobians
and reprojection errors become concise but also makes the required
computation number for transformations decrease. In addition,
a novel structural line triangulation method is provided,
in which the global orientation of a structural line is used to
refine its 3D position. All the novelties result in a more accurate
and fast VIO system. The system is tested on EuRoC MAV
dataset and a self-collected dataset. Experimental results demonstrate
that the proposedmethod obtains better accuracy compared
with state-of-the-art (SOTA) point-based systems (VINS-Mono [3]
and OpenVINS [1]), point-line-based systems (PL-VINS [4] and
Wei et al. [2]), and structural line-based system (StructVIO [5]).
Notably, the self-collected dataset is recorded on Manhattan world
scenes, and is also full of challenging weak texture andmotion blur
situations. On the dataset, the accuracy of our method is increased
by 40.7% compared with the SOTA point-line-based systems.