Structural Regularity Aided Visual-Inertial Odometry With Novel Coordinate Alignment and Line Triangulation

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 proposed method 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 and motion blur situations. On the dataset, the accuracy of our method is increased by 40.7% compared with the SOTA point-line-based systems.