7 accepted papers
Recently, large language models (LLMs) have been explored widely for 3D scene understanding. Among them, training-free approaches are gaining attention for their flexibility and generalization over training-based methods. However, they typically struggle with accuracy and efficiency in practical dep
Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term use, irreversible structural deformation caused by temperature changes, mechanical squeezing, etc. will cause changes in extrinsic parameters, especiall
Generally, high-level features provide more geometrical information compared to point features, which can be exploited to further constrain motions. Planes are commonplace in man-made environments, offering an active means to reduce drift, due to their extensive spatial and temporal observability. T
Planar structures, prevalent in man-made environments, can be observed by a camera for significant periods of time due to their large spatial presence. These structures provide strong planar regularities for Simultaneous Localization and Mapping (SLAM) systems, facilitating long-term navigation. The
Point and line features are complementary in Visual-Inertial Odometry (VIO) or Visual-Inertial Simultaneous Localization And Mapping (VI-SLAM) systems. The advantage of combining these two types of features relies on their proper weighting in the cost function, usually set by their uncertainty. Comp…
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 und
Weak texture and motion blur are always challenging problems for visual-inertial odometry (VIO) systems. To improve accuracy of VIO systems in the challenging scenes, we propose a point-line-based VIO system with novel feature hybrids and with novel predicting-matching for long line track. Point-lin