Visual-inertial odometry (VIO) is the problem of estimating a robot's trajectory by combining
information from an inertial measurement unit (IMU) and a camera and is of great interest to …

Visual odometry and SLAM methods have a large variety of applications in domains such as
augmented reality or robotics. Complementing vision sensors with inertial measurements …

Visual-inertial odometry (VIO) algorithms, fusing various features such as points and lines,
are able to improve their performance in challenging scenes while the running time severely …

In this letter, we present an event-and frame-based visual-inertial odometry (VIO) algorithm
that fuses frames, events, and inertial measurement in a robust and adaptive manner …

Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate
ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have …

Visual–inertial odometry (VIO) is widely studied and used in autonomous robots. This article
proposes a novel tightly coupled monocular VIO system based on point-line constraints …

In this article, we propose a novel optimization-based tightly coupled Direct Visual-Inertial
Odometry (DVIO), which fuses the visual and inertial measurements to provide real-time full …

Visual Inertial Odometry (VIO) is of great interest due the ubiquity of devices equipped with
both a monocular camera and Inertial Measurement Unit (IMU). Methods based on the …

Dynamic environments such as urban areas are still challenging for popular visual-inertial
odometry (VIO) algorithms. Existing datasets typically fail to capture the dynamic nature of …

Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state
estimation via nonlinear optimization. However, real-time optimization quickly becomes …