Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but
they rarely focus on incorporating robust fusion strategies for dealing with imperfect input …

This paper proposes G-VIDO, a vehicle dynamics, and intermittent Global Navigation
Satellite System (GNSS)-aided visual-inertial state estimator, to address the state estimation …

Combining camera, IMU and wheel encoder is a wise choice for car positioning because of
the low cost and complementarity of the sensors. We propose a novel extended visual …

Commercial visual–inertial odometry (VIO) systems have been gaining attention as cost-
effective, off-the-shelf, six-degree-of-freedom (6-DoF) ego-motion-tracking sensors for …

Timing is an important property for robotic systems that continuously interact with our
physical world. Variation in program execution time caused by limited computational …

The online system state initialization and simultaneous spatial-temporal calibration are
critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not …

Due to the sensors' latency, unsynchronized clocks, triggering, and data jams, there usually
exists an inevitable time offset between image streams and inertial measurement unit (IMU) …

There is an unavoidable time offset between the camera stream and the inertial
measurement unit (IMU) data due to the sensor triggering and transmission delays, which …

In this letter, an efficient closed-form solution for the state initialization in visual-inertial
odometry (VIO) and simultaneous localization and mapping (SLAM) is presented. Unlike the …

Combining camera, IMU and wheel encoder is a wise choice for car positioning because of
the low cost and complementarity of the sensors. We propose a novel extended visual …