Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in
recent decades. Numerous observability analysis and fusion schemes for motion estimation …

We formulate for the first time visual-inertial initialization as an optimal estimation problem, in
the sense of maximum-a-posteriori (MAP) estimation. This allows us to properly take into …

In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial
odometry (VIO) system, which can provide accurate state estimates and dense depth maps …

We present PennCOSYVIO, a new challenging Visual Inertial Odometry (VIO) benchmark
with synchronized data from a VI-sensor (stereo camera and IMU), two Project Tango hand …

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 …

As sensor calibration plays an important role in visual-inertial sensor fusion, this article
performs an in-depth investigation of online self-calibration for robust and accurate visual …

This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with
a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware …

Aggressive motions from agile flights or traversing irregular terrain induce motion distortion
in LiDAR scans that can degrade state estimation and mapping. Some methods exist to …

The increasing demand for real-time high-precision Visual Odometry systems as part of
navigation and localization tasks has recently been driving research towards more versatile …

High-dimensional nonlinear state estimation is at the heart of inertial-aided navigation
systems (INS). Traditional methods usually rely on good initialization and find difficulty in …