In recent years, unmanned aerial vehicles (UAVs), commonly known as drones, have
gained increasing interest in both academia and industries. The evolution of UAV …

The increasing world population makes it necessary to fight challenges such as climate
change and to realize production efficiently and quickly. However, the minimum cost …

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and
map**, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory …

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 …

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By
sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide …

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

One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-
inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the …

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 …

We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion
estimation. The proposed method enables robust and efficient navigation for ground …

Today's autonomous drones have reaction times of tens of milliseconds, which is not
enough for navigating fast in complex dynamic environments. To safely avoid fast moving …