Abstract Simultaneous Localization and Map** (SLAM) is a process to use multiple
sensors to position an unmanned mobile vehicle without previous knowledge of the …

After decades of development, LIDAR and visual SLAM technology has relatively matured
and been widely used in the military and civil fields. SLAM technology enables the mobile …

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 …

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

Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-
based approaches outperform filtering methods in terms of accuracy due to their capability to …

Combining visual and inertial measurements has become popular in mobile robotics, since
the two sensing modalities offer complementary characteristics that make them the ideal …

After the incredible success of deep learning in the computer vision domain, there has been
much interest in applying Convolutional Network (ConvNet) features in robotic fields such as …

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 …

Place recognition has long been an incompletely solved problem in that all approaches
involve significant compromises. Current methods address many but never all of the critical …