Self-Localization of Unmanned Aerial Vehicles Based on Optical Flow in Inboard Camera Image

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00316982" target="_blank" >RIV/68407700:21230/18:00316982 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/book/10.1007%2F978-3-319-76072-8" target="_blank" >https://link.springer.com/book/10.1007%2F978-3-319-76072-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-76072-8_8" target="_blank" >10.1007/978-3-319-76072-8_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-Localization of Unmanned Aerial Vehicles Based on Optical Flow in Inboard Camera Image

Popis výsledku v původním jazyce

This paper proposes and evaluates the implementation of a self-localization system intended for use in Unmanned Aerial Vehicles. Accurate localization is necessary for UAVs for efficient stabilization, navigation and collision avoidance. Conventionally, this requirement is fulfilled using external hardware infrastructure, such as Global Navigation Satellite System or visual motion-capture system. These approaches are, however, not applicable in environments where deployment of cumbersome motion capture equipment is not feasible, as well as in GNSS-denied environments. Systems based on Simultaneous Localization and Mapping (SLAM) require heavy and expensive onboard equipment and high amounts of data transmissions for sharing maps between UAVs. Availability of a system without these drawbacks is crucial for deployment of tight formations of multiple fully autonomous micro UAVs for both outdoor and indoor missions. The project was inspired by the often used sensor PX4FLOW Smart Camera. The aim was to develop a similar sensor, without the drawbacks observed in its use, as well as to make the operation of it more transparent and to make it independent of a specific hardware. Our proposed solution requires only a lightweight camera and a single-point range sensor. It is based on optical flow estimation from consecutive images obtained from downward-facing camera, coupled with a specialized RANSAC-inspired post-processing method that takes into account flight dynamics. This filtering makes it more robust against imperfect lighting, homogenous ground patches, random close objects and spurious errors. These features make this approach suitable even for coordinated flights through demanding forest-like environment. The system is designed mainly for horizontal velocity estimation, but specialized modifications were also made for vertical speed and yaw rotation rate estimation. These methods were tested in a simulator and subsequently in real-world conditions.

Název v anglickém jazyce

Self-Localization of Unmanned Aerial Vehicles Based on Optical Flow in Inboard Camera Image

Popis výsledku anglicky

This paper proposes and evaluates the implementation of a self-localization system intended for use in Unmanned Aerial Vehicles. Accurate localization is necessary for UAVs for efficient stabilization, navigation and collision avoidance. Conventionally, this requirement is fulfilled using external hardware infrastructure, such as Global Navigation Satellite System or visual motion-capture system. These approaches are, however, not applicable in environments where deployment of cumbersome motion capture equipment is not feasible, as well as in GNSS-denied environments. Systems based on Simultaneous Localization and Mapping (SLAM) require heavy and expensive onboard equipment and high amounts of data transmissions for sharing maps between UAVs. Availability of a system without these drawbacks is crucial for deployment of tight formations of multiple fully autonomous micro UAVs for both outdoor and indoor missions. The project was inspired by the often used sensor PX4FLOW Smart Camera. The aim was to develop a similar sensor, without the drawbacks observed in its use, as well as to make the operation of it more transparent and to make it independent of a specific hardware. Our proposed solution requires only a lightweight camera and a single-point range sensor. It is based on optical flow estimation from consecutive images obtained from downward-facing camera, coupled with a specialized RANSAC-inspired post-processing method that takes into account flight dynamics. This filtering makes it more robust against imperfect lighting, homogenous ground patches, random close objects and spurious errors. These features make this approach suitable even for coordinated flights through demanding forest-like environment. The system is designed mainly for horizontal velocity estimation, but specialized modifications were also made for vertical speed and yaw rotation rate estimation. These methods were tested in a simulator and subsequently in real-world conditions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GA16-24206S" target="_blank" >GA16-24206S: Metody informatického plánování cest pro neholonomní mobilní roboty v úlohách monitorování a dohledu</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Modelling and Simulation for Autonomous Systems (MESAS 2017)

ISBN

978-3-319-76071-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

27

Strana od-do

106-132

Název nakladatele

Springer International Publishing AG

Místo vydání

Cham

Místo konání akce

Řím

Datum konání akce

24. 10. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000444831600008