Computer Vision System for Landing Platform State Assessment Onboard of Unmanned Aerial Vehicle in Case of Input Visual Information Distortion

Kim, Iuliia; Viksnin, Ilya I.; Kaisina, Irina; Kuznetsov, V. L.

doi:10.23919/fruct52173.2021.9435520

Cited by 1 publication

(1 citation statement)

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“…Nevertheless, this technology is not always available or it is sometimes incapable of giving an acceptable level of accuracy, as can happen when the inside of a tank of a petrochemical plant is to be inspected. For this reason, in the literature, complementary landing assistance systems (LASs) are proposed based on computer vision techniques [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], a fusion between computer vision techniques and inertial measurement units (IMUs) [ 20 , 21 , 22 , 23 , 24 , 25 ], computer vision, IMU and ultrasonic sensors [ 26 ], computer vision and a Time-of-Flight-based height sensor [ 27 ], computer vision and GNSS [ 28 , 29 ] and even an approach fusing onboard cameras and a robotic total station [ 30 ]. The main setback of traditional vision-based systems is their strong dependency on weather or lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

UWB and IMU-Based UAV’s Assistance System for Autonomous Landing on a Platform

Ochoa-de-Eribe-Landaberea

Zamora-Cadenas

Peñagaricano-Muñoa³

et al. 2022

Sensors

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This work presents a novel landing assistance system (LAS) capable of locating a drone for a safe landing after its inspection mission. The location of the drone is achieved by a fusion of ultra-wideband (UWB), inertial measurement unit (IMU) and magnetometer data. Unlike other typical landing assistance systems, the UWB fixed sensors are placed around a 2 × 2 m landing platform and two tags are attached to the drone. Since this type of set-up is suboptimal for UWB location systems, a new positioning algorithm is proposed for a correct performance. First, an extended Kalman filter (EKF) algorithm is used to calculate the position of each tag, and then both positions are combined for a more accurate and robust localisation. As a result, the obtained positioning errors can be reduced by 50% compared to a typical UWB-based landing assistance system. Moreover, due to the small demand of space, the proposed landing assistance system can be used almost anywhere and is deployed easily.

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