A Vision System for Intelligent Mission Profiles of Micro Air Vehicles

Todorovic, Sinisa; Nechyba, M.C.

doi:10.1109/tvt.2004.834880

Cited by 51 publications

(24 citation statements)

References 25 publications

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“…The normal vector has direct mathematical relation with the attitude as expressed in different methods. The work done by [20,21] are examples of successful autonomous control of a MAV based on attitude estimation from the horizon detected. In literature, horizon detection problem has been addressed by segmentation and edge detection.…”

Section: Using Perspective Sensorsmentioning

confidence: 99%

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: Using Perspective Sensorsmentioning

confidence: 99%

Vision Based UAV Attitude Estimation: Progress and Insights

Shabayek

Demonceaux

Morel

et al. 2011

J Intell Robot Syst

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show abstract

“…On the other hand, the relation between the pitch angle β and the horizon line is a little more complicated. Previous work such as [18], [19], [20], [21], [22] estimate the pitch percentage which is correlated with the actual pitch. However, for the ego motion estimation we need the exact pitch angle of the UAV; hence, we extend the previous work and find the pitch using the intrinsic parameters of the camera.…”

Section: A Estimating the Equation Of The Ground Planementioning

confidence: 99%

“…The horizon line was previously employed for stability and control of the UAV in [18], [19], [20], [21], [22], [23]. There are three major differences between all this work and ours: First, our method is a complete ego-motion estimation/SFM algorithm where we compute the actual UAV motion, while the previous work recovered partial motion parameters (eg.…”

Section: Introductionmentioning

confidence: 99%

Horizon constraint for unambiguous UAV navigation in planar scenes

Oreifej

Lobo

Shah

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-When the UAV goes to high altitudes such that the observed surface of the earth becomes planar, the structure and motion recovery of the earth's moving plane becomes ambiguous. This planar degeneracy has been pointed out very often in the literature; therefore, current navigation methods either completely fail or give many confusing solutions in such scenario. Interestingly, the horizon line in planar scenes is straight and distinctive; hence, easily detected. Therefore, we show in this paper that the horizon line provides two degrees of freedom that control the relative orientation between the camera coordinate system and the local surface of earth. The recovered degrees of freedom help linearize and disambiguate the planar flow, and therefore we obtain a unique solution for the UAV motion estimation. Unlike previous work which used the horizon to provide the roll angle and the pitch percentage and only employed them for flight stability, we extract the exact angles and directly use them to estimate the ego motion. Additionally, we propose a novel horizon detector based on the maximum a posteriori estimation of both motion and appearance features which outperforms the other detectors in planar scenarios. We thoroughly experimented on the proposed method against information from GPS and gyroscopes, and obtained promising results.

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“…1) rather than straight as most work assumes [10][11][12]. Raw segmentations of mountainous images are shown in the classification approach of [13], but there is no discussion of explicit recovery of the boundary curve in a single image or tracking it over time.…”

Section: Introductionmentioning

confidence: 99%

“…The tracker in the latter paper initializes automatically under the assumption that the ground feature is homogeneous, only gently curved, and occupies a fairly large fraction of the image. [13] has some results on finding straight roads in low-altitude aerial images using a Hough transform on static images only, with no tracking.…”

Section: Introductionmentioning

confidence: 99%

Superpixel Analysis for Object Detection and Tracking with Application to UAV Imagery

Rasmussen

Advances in Visual Computing

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Abstract. We introduce a framework for object detection and tracking in video of natural outdoor scenes based on fast per-frame segmentations using Felzenszwalb's graph-based algorithm. Region boundaries obtained at multiple scales are first temporally filtered to detect stable structures to be considered as object hypotheses. Depending on object type, these are then classified using a priori appearance characteristics such as color and texture and geometric attributes derived from the Hough transform. We describe preliminary results on image sequences taken from low-flying aircraft in which object categories are relevant to UAVs, consisting of sky, ground, and navigationally-useful ground features such as roads and pipelines.

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A Vision System for Intelligent Mission Profiles of Micro Air Vehicles

Cited by 51 publications

References 25 publications

Vision Based UAV Attitude Estimation: Progress and Insights

Vision Based UAV Attitude Estimation: Progress and Insights

Horizon constraint for unambiguous UAV navigation in planar scenes

Superpixel Analysis for Object Detection and Tracking with Application to UAV Imagery

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