A Comprehensive Introduction of Visual-Inertial Navigation

TL;DR

This paper provides a comprehensive tutorial on visual-inertial navigation, covering classical state estimation, models, methodologies, calibration, evaluation, and recent learning-based approaches.

Contribution

It offers an in-depth overview of classical and recent methods for visual-inertial navigation, including models, estimation techniques, and calibration procedures.

Findings

Analysis of filter-based and optimization-based estimation frameworks

Discussion on calibration and initialization techniques

Overview of recent learning-based approaches

Abstract

In this article, a tutorial introduction to visual-inertial navigation(VIN) is presented. Visual and inertial perception are two complementary sensing modalities. Cameras and inertial measurement units (IMU) are the corresponding sensors for these two modalities. The low cost and light weight of camera-IMU sensor combinations make them ubiquitous in robotic navigation. Visual-inertial Navigation is a state estimation problem, that estimates the ego-motion and local environment of the sensor platform. This paper presents visual-inertial navigation in the classical state estimation framework, first illustrating the estimation problem in terms of state variables and system models, including related quantities representations (Parameterizations), IMU dynamic and camera measurement models, and corresponding general probabilistic graphical models (Factor Graph). Secondly, we investigate the existing model-based estimation methodologies, these involve filter-based and optimization-based frameworks and related on-manifold operations. We also discuss the calibration of some relevant parameters, also initialization of state of interest in optimization-based frameworks. Then the evaluation and improvement of VIN in terms of accuracy, efficiency, and robustness are discussed. Finally, we briefly mention the recent development of learning-based methods that may become alternatives to traditional model-based methods.