In recent decades, visual simultaneous localization and mapping (vSLAM) has gained
significant interest in both academia and industry. It estimates camera motion and …

Event cameras that asynchronously output low-latency event streams provide great
opportunities for state estimation under challenging situations. Despite event-based visual …

Robust state estimation in challenge situations is still an unsolved problem, especially
achieving onboard pose feedback control for aggressive motion. In this paper, we propose …

Deep models trained using synthetic data require domain adaptation to bridge the gap
between the simulation and target environments. State-of-the-art domain adaptation …

Vision-based localization is a cost-effective and, thus, attractive solution for many intelligent
mobile platforms. However, its accuracy and especially robustness still suffer from low …

Event cameras offer the exciting possibility of tracking the camera's pose during high-speed
motion and in adverse lighting conditions. Despite this promise, existing event-based …

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 …

Event cameras triggered a paradigm shift in the computer vision community delineated by
their asynchronous nature, low latency, and high dynamic range. Calibration of event …

Simultaneous localisation and mapping (SLAM) systems have been widely studied over the
past 30 years and extensively applied in various fields such as mobile robotics, augmented …

To solve the problem of inaccurate orientation estimation after long-term operations of the
Inertial Measurement Unit (IMU), we present a learning-based method (called Gyro-Net) to …