As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems
(VINS) have prevailed in a wide range of applications from mobile augmented reality to …

The Simultaneous Localization and Map** (SLAM) technique has achieved astonishing
progress over the last few decades and has generated considerable interest in the …

In this paper, we present an open platform, termed OpenVINS, for visual-inertial estimation
research for both the academic community and practitioners from industry. The open …

Accurate state estimation is a fundamental problem for autonomous robots. To achieve
locally accurate and globally drift-free state estimation, multiple sensors with complementary …

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 …

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 …

Legged robots require knowledge of pose and velocity in order to maintain stability and
execute walking paths. Current solutions either rely on vision data, which is susceptible to …

Due to its widespread application in the robotics field, the Kalman filter has received
increased attention from researchers. This work reviews some of the modifications …

In this paper, we focus on the problem of motion tracking in unknown environments using
visual and inertial sensors. We term this estimation task visual–inertial odometry (VIO), in …

The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental
engineering tool that is pervasively used in control and robotics and for various estimation …