A Vision based system for attitude estimation of UAVS

Thurrowgood, Saul; Soccol, Dean; Moore, Richard J. D.; Bland, Daniel; Srinivasan, Mandyam V.

doi:10.1109/iros.2009.5354041

Cited by 49 publications

(40 citation statements)

References 11 publications

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“…In [9], they provided a complexity and performance comparison between their method and other methods in litterature. They have included a comparison table of execution times for various published studies on visual attitude estimation.…”

Section: Horizon Detectionmentioning

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: Horizon Detectionmentioning

confidence: 99%

“…In [9], they propose an algorithm which can be incorporated into any vision system (e.g. narrow angle, wide angle or panoramic), irrespective of the way in which the environment is imaged (e.g.…”

Section: Sky/ground Segmentationmentioning

confidence: 99%

Vision Based UAV Attitude Estimation: Progress and Insights

Shabayek

Demonceaux

Morel

et al. 2011

J Intell Robot Syst

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“…There are several applications of detected horizon including smooth navigation of unmanned aerial vehicles (UAVs) [17], [14], [23] and micro air vehicles (MAVs) [12], [13], [15], augmented reality [20], visual geo-localization and annotation of mountain/desert imagery [22], port security [16], and outdoor vehicle localization. Previous horizon line detection methods can be grouped into two major categories; (i) methods modeling sky and nonsky regions using machine learning [17], [12], [13], [16], [14], [15], [21] and (ii) methods relying on edge detection [19], [5].…”

Section: Introductionmentioning

confidence: 99%

Fusion of edge-less and edge-based approaches for horizon line detection

Ahmad

Bebis

Nicolescu

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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Abstract-Horizon line detection requires finding a boundary which segments an image into sky and non-sky regions. It has many applications including visual geo-localization and geotagging, robot navigation/localization, and ship detection and port security. Recently, two machine learning based approaches have been proposed for horizon line detection: one relying on edge classification and the other relying on pixel classification. In the edge-based approach, a classifier is used to refine the edge map by removing non-horizon edges. The refined edge map is then used to form a multi-stage graph where dynamic programming is applied to extract the horizon line. In the edge-less approach, classification is used to obtain a confidence of horizon-ness at each pixel location. The horizon line is then extracted by applying dynamic programming on the resultant dense classification map rather than on the edge map. Both approaches have shown to outperform the classical approach where dynamic programming is applied on the non-refined edge map. In this paper, we provide a comparison between the edge-less and edge-based approaches using two challenging data sets. Moreover, we propose fusing the information about the horizon-ness and edge-ness of each pixel. Our experimental results illustrate that the proposed fusion approach outperforms both the edge-based and edge-less approaches.

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“…This task is of demand in navigating small unmanned aerial vehicles (UAVs) [1,2,3] and micro air vehicles (MAVs) [4,5,6], visual geolocalization [7,8], ship detection [9,10] and outdoor robot/vehicle localization [11,12,13]. The horizon line can be detected by (i) modeling sky and non-sky regions using machine learning [1,2,4,5,6,9,22] and (ii) employing edge detection [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…The authors identified cases when their method requires modifications of clustering process, because the assumption about the light field does not hold in general. Thurrowgood et al [3] has found an optimum threshold to segment preliminary transformed from RGB space images based on histograms and priors about sky and non-sky regions. Their approach is applicable only to UAV navigation due to the assumption that sky and ground pixels are equiprobable.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Horizon Line Detection based on Fusion of Classification and Clustering

Yazdanpanah¹,

Regentova²,

Muthukumar

et al. 2015

IJCA

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Horizon line detection is a demanding problem in various tasks associated with planet exploration, because no standard approaches such as global positioning system is available. Horizon line is a boundary line defined between a sky and non-sky region in 2D images, and it is an important visual clue that can be utilized for calculating the rover's position and orientation during planetary missions. The problem of segmenting an image into sky and non-sky regions is classically referred as "horizon line detection". Subsequently, the localization problem can be solved by matching the detected horizon line in 2D images with virtually generated horizon lines from 3D terrain patterns. In this paper, we propose a new real-time horizon line detection (HLD) method by coupling clustering and classifications, as well as implementing the algorithm on the NVIDIA's compute unified device architecture (CUDA). The proposed method has been evaluated on NASA Basalt Hill dataset and on a set of mountainous images that have been collected from the web. The experiments demonstrate high accuracy in determining the horizon line that is proven by provided Receiver Operating Characteristic (ROC) curves.

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A Vision based system for attitude estimation of UAVS

Cited by 49 publications

References 11 publications

Vision Based UAV Attitude Estimation: Progress and Insights

Vision Based UAV Attitude Estimation: Progress and Insights

Fusion of edge-less and edge-based approaches for horizon line detection

Real-Time Horizon Line Detection based on Fusion of Classification and Clustering

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