Simultaneous localization and mapping (SLAM) techniques are widely researched, since
they allow the simultaneous creation of a map and the sensors' pose estimation in an …

This paper presents a complete review of computer vision algorithms and vision-based
intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles …

Visual–inertial odometry (VIO) is known to suffer from drifting, especially over long-term runs.
In this article, we present GVINS, a nonlinear optimization-based system that tightly fuses …

Direct methods for visual odometry (VO) have gained popularity for their capability to exploit
information from all intensity gradients in the image. However, low computational speed as …

This paper presents visual-inertial datasets collected on-board a micro aerial vehicle. The
datasets contain synchronized stereo images, IMU measurements and accurate ground …

In recent years, vision-aided inertial odometry for state estimation has matured significantly.
However, we still encounter challenges in terms of improving the computational efficiency …

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

Event-based cameras are a new passive sensing modality with a number of benefits over
traditional cameras, including extremely low latency, asynchronous data acquisition, high …

Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level
geometric features such as points, lines, and planes. They are unable to assign semantic …

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly
using pixel intensity errors of image patches, achieves accurate tracking performance while …