Structureless VIO
This addresses the need for more efficient and accurate localization solutions in robotics and autonomous systems, though it appears incremental as it builds on existing VIO principles.
The authors tackled the tightly-coupled problem of localization and mapping in visual-inertial odometry by proposing a structureless VIO that removes the visual map, resulting in improved computational efficiency and accuracy compared to a structure-based baseline.
Visual odometry (VO) is typically considered as a chicken-and-egg problem, as the localization and mapping modules are tightly-coupled. The estimation of a visual map relies on accurate localization information. Meanwhile, localization requires precise map points to provide motion constraints. This classical design principle is naturally inherited by visual-inertial odometry (VIO). Efficient localization solutions that do not require a map have not been fully investigated. To this end, we propose a novel structureless VIO, where the visual map is removed from the odometry framework. Experimental results demonstrated that, compared to the structure-based VIO baseline, our structureless VIO not only substantially improves computational efficiency but also has advantages in accuracy.