Research on the Localization of Unmanned Flight Vehicle Based on the Monocular Vision

Liang, Zhuang; Shen, Haomin; Chen, Tianyu; Gu, Cunfeng; Song, Quanhong

doi:10.1007/978-981-15-8155-7_273

Cited by 3 publications

(1 citation statement)

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“…Nonetheless, these solutions require specific equipment, for example, a base station for sending GNSS position corrections to the UAV, which further reduces the applicability of such a system. Another less restricting approach is gathering knowledge of UAV surroundings suitable for localization by onboard sensors, such as LiDAR-based (Hector SLAM [9] for singleplane LiDARs, and LiDAR Odometry And Mapping -LOAM [10] for multiple-plane LiDARs), and vision-based methods [11] [12].…”

Section: Introductionmentioning

confidence: 99%

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

Bednář,

Petrlík,

Vivaldini

et al. 2023

Preprint

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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.

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