Visual steering of UAV in unknown environments

Yuan, Chunrong; Recktenwald, Fabian; Mallot, Hanspeter A.

doi:10.1109/iros.2009.5354361

Cited by 19 publications

(12 citation statements)

References 8 publications

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“…Apart from obstacle localization, this work has also presented an algorithm for the estimation of the obstacles' shape. Similarly, in [97] a novel monocular motion estimation approach and scene reconstruction has been presented. The motion and depth information were recovered by the utilization of a robust optical flow measurement and point correspondence algorithm from successive images.…”

Section: Obstacle Detectionmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

290

120

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During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

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Section: Obstacle Detectionmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

290

120

View full text Add to dashboard Cite

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“…Prior work has shown the necessity of observing the environments in multiple directions to perform complex tasks such as corridor following (Serres et al 2008;Humbert and Hyslop 2010), steep relief following or obstacle avoidance (Yuan et al 2009;Beyeler et al 2009). …”

Section: Terrain Following With Multiple Aperturesmentioning

confidence: 99%

A terrain-following control approach for a VTOL Unmanned Aerial Vehicle using average optical flow

et al. 2010

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This paper presents a nonlinear controller for terrain following of a vertical take-off and landing vehicle (VTOL). The VTOL vehicle is assumed to be a rigid body, equipped with a minimum sensor suite (camera, IMU and barometric altimeter) maneuvering over a textured rough terrain made of a concatenation of planar surfaces. Assuming that the forward velocity is separately regulated to a desired value, the proposed control approach ensures terrain following and guarantees that the vehicle does not collide with the ground during the task. The proposed control acquires an optical flow from multiple spatially separate observation points, typically obtained via multiple cameras or non collinear directions in a unique camera. The proposed control algorithm has been tested extensively in simulation and then implemented on a quadrotor UAV to demonstrate the performance of the closed-loop system. Electronic supplementary materialThe online version of this article (

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“…Furthermore, we localize both static and dynamic obstacles and analyse their spatial configuration. Based on our earlier work (Yuan et al, 2009), a novel visual steering approach has been developed for guiding the robot away from possible obstacles.…”

Section: Related Workmentioning

confidence: 99%