Vision-based obstacle avoidance for unmanned aerial vehicles

Gosiewski, Zdzisław; Cieśluk, Jakub; Ambroziak, Leszek

doi:10.1109/cisp.2011.6100621

Cited by 25 publications

(17 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afterwards, the distance from the spot has been computed, using geometry with the assistance of a laser sensor. Furthermore, in [96] a vision based obstacle avoidance approach using an optical flow method based on Lucas-Kanade gradient has been proposed, with the general aim to extract image depth. Apart from obstacle localization, this work has also presented an algorithm for the estimation of the obstacles' shape.…”

Section: Obstacle Detectionmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

289

120

View full text Add to dashboard Cite

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.

show abstract

Section: Obstacle Detectionmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

289

120

View full text Add to dashboard Cite

show abstract

“…The principle of optic flow makes use of motion parallax to determine the flow vector field. The Lucas-Kanade algorithm, which uses intensity gradients to estimate the flow vectors, was employed by Gosiewski and Cieśluk in [13]. Robust optic flow computation techniques were developed by Alireza and Suter in [14] and Beauchemin and Barron in [15].…”

Section: Related Work a Personal Air Transportation Systemmentioning

confidence: 99%

“…As the point cloud enters the sphere of influence, the vehicle responds to forceF reptot specified in Equation 13.…”

Section: E Repulsive Force Field From a Point Cloudmentioning

confidence: 99%

Vision-Based Collision Avoidance for Personal Aerial Vehicles Using Dynamic Potential Fields

Rehmatullah

Kelly

2015

2015 12th Conference on Computer and Robot Vision

View full text Add to dashboard Cite

In this paper we present a prototype system that aids the operator of a Personal Air Vehicle (PAV) by actively monitoring vehicle surroundings and providing autonomous control inputs for obstacle avoidance. The prototype is developed for a Personal Air Transportation System (PATS) that will enable human operators with low level of technical knowledge to use aerial vehicles for a day-to-day commute. While most collision avoidance systems used on human controlled vehicles override operator input, our proposed system allows the operator to be in control of the vehicle at all times. Our approach uses a dynamic potential field to generate pseudo repulsive forces that, when converted into control inputs, force the vehicle on a trajectory around the obstacle. By allowing the vehicle control input to be the sum of operator controls and collision avoidance controls, the system ensures that the operator is in control of the vehicle at all times. We first present a dynamic repulsive potential function and then provide a generic control architecture required to implement the collision avoidance system on a mobile platform. Further, extensive computer simulations of the proposed algorithm are performed on a quadcopter model, followed by hardware experiments on a stereo vision sensor. The proposed collision avoidance system is computationally inexpensive and can be used with any sensor that can produce a point cloud for obstacle detection.

show abstract

“…Marlow compares the estimates with the 'ground truth' but does not state any quantitative value. Gosiewski et al (2011) implements an optical flow algorithm by making use of the Lucas-Kanade algorithm. In another paper from optic flow is used to estimate the time to collision.…”

Section: -1-1 Monocular Visionmentioning

confidence: 99%