Deployment of Reliable Visual Inertial Odometry Approaches for Unmanned Aerial Vehicles in Real-world Environment

TL;DR

This paper evaluates the reliability of Visual Inertial Odometry methods for UAVs in real-world conditions, addressing robustness issues and proposing solutions for dependable autonomous flight.

Contribution

It provides a comprehensive reliability analysis of VIO methods in real-world UAV deployment and suggests workarounds for challenging conditions.

Findings

VIO methods achieve high accuracy on artificial datasets.

Reliability decreases under real-world disturbances and sensor degradation.

Proposed workarounds improve pose estimation robustness in challenging environments.

Abstract

Integration of Visual Inertial Odometry (VIO) methods into a modular control system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of cooperating UAVs in real-world conditions are presented in this paper. Reliability analysis and fair performance comparison of several methods integrated into a control pipeline for achieving full autonomy in real conditions is provided. Although most VIO algorithms achieve excellent localization precision and negligible drift on artificially created datasets, the aspects of reliability in non-ideal situations, robustness to degraded sensor data, and the effects of external disturbances and feedback control coupling are not well studied. These imperfections, which are inherently present in cases of real-world deployment of UAVs, negatively affect the ability of the most used VIO approaches to output a sensible pose estimation. We identify the conditions that are critical for a reliable flight under VIO localization and propose workarounds and compensations for situations in which such conditions cannot be achieved. The performance of the UAV system with integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and the ability to provide reliable pose estimation for the feedback control is demonstrated onboard a UAV that is tracking dynamic trajectories under challenging illumination.