Omnidirectional vision on UAV for attitude computation

Demonceaux, Cédric; Vasseur, Pascal; Regard, C.

doi:10.1109/robot.2006.1642132

Cited by 37 publications

(38 citation statements)

References 13 publications

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“…They proposed to equip a Micro Air Vehicle (MAV) with a perspective camera to have a vision-guided flight stability and autonomy system. Omnidirectional sensors for attitude estimation were first introduced by [2]. The omnidirectional sensors (Fisheye and Catadioptric cameras shown in figure (2)) were used in different scenarios.…”

Section: Vision Sensors For Attitude Estimationmentioning

confidence: 99%

“…Using omnidirectional vision, some algorithms use markovian formulation of sky/ground segmentation based on color information [2], or the sky/ground partitioning is done in the spherical image thanks to the optimization of the Mahalanobis distance between these regions. The search for points in either regions takes place in the RGB space [11].…”

Section: Sky/ground Segmentationmentioning

confidence: 99%

“…Next, the horizon points should be back projected on the equivalence sphere. Finally, the best plane passes through the horizon on that sphere should be estimated to deduce its normal which gives the roll and pitch angles (e.g [2,11]). …”

Section: Horizon Estimation and Attitude Computationmentioning

confidence: 99%

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Vision Based UAV Attitude Estimation: Progress and Insights

Shabayek

Demonceaux

Morel

et al. 2011

J Intell Robot Syst

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Unmanned aerial vehicles (UAVs) are increasingly replacing manned systems in situations that are dangerous, remote, or difficult for manned aircraft to access. Its control tasks are empowered by computer vision technology. Visual sensors are robustly used for stabilization as primary or at least secondary sensors. Hence, UAV stabilization by attitude estimation from visual sensors is a very active research area. Vision based techniques are proving their effectiveness and robustness in handling this problem. In this work a comprehensive review of UAV vision based attitude estimation approaches is covered, starting from horizon based methods and passing by vanishing points, optical flow, and stereoscopic based techniques. A novel segmentation approach for UAV attitude estimation based on polarization is proposed. Our future insightes for attitude estimation from uncalibrated catadioptric sensors are also discussed.

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Section: Vision Sensors For Attitude Estimationmentioning

confidence: 99%

Section: Sky/ground Segmentationmentioning

confidence: 99%

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Vision Based UAV Attitude Estimation: Progress and Insights

Shabayek

Demonceaux

Morel

et al. 2011

J Intell Robot Syst

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“…Ying and Hu (2004) demonstrate that the occluding contour of a sphere in space is projected onto a circle on the unit sphere or onto a conic in the catadioptric image plane. Considering the skyline the occluding contour on the earth sphere surface, finding it requires to look for a small circle on the unitary sphere model or for a conic or ellipse on the image plane, as proposed by Demonceaux et al (2006) (see Fig. 3 for an illustration).…”

Section: Skyline and Catadioptric Imagementioning

confidence: 99%

“…Conroy et al (2009) use spatial decompositions of the instantaneous optic flow to extract local proximity information from catadioptric images obtained onboard a microair-vehicle (MAV) for corridor-like environment navigation. Demonceaux et al (2006), use a similar approach to the one presented in this paper, showing the advantages of using omnidirectional rather than perspective images for attitude estimation. They detect the horizon line on the catadioptric image using a Markov Random Fields segmentation and then project it on the equivalent sphere projection model for a Catadioptric system (Geyer and Daniilidis 2000) to obtain the attitude angles of the camera frame, which are related to the normal vector of the projected horizon line.…”

Section: Introductionmentioning

confidence: 96%

Unmanned aerial vehicles UAVs attitude, height, motion estimation and control using visual systems

Mondragón¹,

Olivares-Méndez²,

Campoy³

et al. 2010

Auton Robot

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This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV's attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV's motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COL-IBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

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