Efficient target geolocation by highly uncertain small air vehicles

Grocholsky,; Dille,; Nuske,

doi:10.1109/iros.2011.6048777

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…The system state is directly observable by the global localization measurements provided by the GPS module. The congregated measurement model is defined as (15) with the measurement covariance given by (16) Hence, the global measurement likelihood is distributed according to (17) The measurement likelihood can be extended to more than two vehicles by appending their measurement models to the congregated measurement model and augmenting the measurement noise covariance.…”

Section: A Localizationmentioning

confidence: 99%

“…To compute the covariance of the relative localization measurement, the camera position uncertainty estimated in step 2 of Algorithm III.1 and the camera orientation uncertainty estimated by the AHRS are propagated through the linearized camera model [17]. Linearizing the camera model about the mean camera orientation yields (24) where is the Jacobian of the camera model, with respect to the quadrotor's orientation, evaluated at the mean quadrotor's orientation estimate provided by the AHRS.…”

Section: A Localizationmentioning

confidence: 99%

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Probabilistic Cooperative Target Localization

Nagaty¹,

Thibault²,

Trentini³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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This work addresses the problem of localizing a ground target observed by multiple heterogeneous unmanned vehicles. Specifically, the case of a team of unmanned aerial and ground vehicles is analyzed. Effective collaboration between unmanned aerial and ground vehicles can utilize the strengths of both platforms while mediating their individual weaknesses. In this research, a probabilistic framework is proposed to improve target localization accuracy by utilizing the epipolar geometry and inter-robot localization measurements. A virtual simulation environment is implemented to fully test the proposed methods. Hardware experiments are carried out to validate the proposed methods. Note to Practitioners-This paper was motivated by the problem of tracking ground objects of interest using multiple cooperative autonomous vehicles. Most of the existing approaches to cooperative target tracking are designed for homogeneous vehicles and neglect geometrical constraints of the multiview target tracking problem. The methodology proposed in this work incorporates heterogenous robots to cooperatively track a ground object of interest.To improve the accuracy of target localization, the epipolar geometry between multirobot views is utilized to formulate mathematical constraints. In this paper, it is analytically proved that the imposing the proposed constraints improves target localization accuracy. Experimental and simulation results demonstrate the benefits in target localization accuracy using the proposed method.

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Section: A Localizationmentioning

confidence: 99%

Section: A Localizationmentioning

confidence: 99%

Probabilistic Cooperative Target Localization

Nagaty¹,

Thibault²,

Trentini³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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“…Some works integrate and fuse vision data from the UAV and UGV for best target tracking [3], another work [10] presents uncertainty modeling and observation–fusion approaches that produce considerable improvement in geo-location accuracy. Also, a different work [11] presents a comparative study of several target estimation and motion-planning techniques and remarks on the importance (and the difficulty) of a single UAV at maintaining consistent view of moving targets.…”

Section: Related Workmentioning

confidence: 99%

An Aerial-Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas

Garzón

Valente

Zapata

et al. 2013

Sensors

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There are many outdoor robotic applications where a robot must reach a goal position or explore an area without previous knowledge of the environment around it. Additionally, other applications (like path planning) require the use of known maps or previous information of the environment. This work presents a system composed by a terrestrial and an aerial robot that cooperate and share sensor information in order to address those requirements. The ground robot is able to navigate in an unknown large environment aided by visual feedback from a camera on board the aerial robot. At the same time, the obstacles are mapped in real-time by putting together the information from the camera and the positioning system of the ground robot. A set of experiments were carried out with the purpose of verifying the system applicability. The experiments were performed in a simulation environment and outdoor with a medium-sized ground robot and a mini quad-rotor. The proposed robotic system shows outstanding results in simultaneous navigation and mapping applications in large outdoor environments.

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“…We also apply RRANSAC to an aerial geolocation problem, where one or more airborne sensors estimate the ground location of ground objects. There are many civilian and military applications for geolocation, as noted in the breadth of previous work, including Grocholsky et al [2011], Liang and Liang [2011], Campbell and Wheeler [2010], Conte et al [2008], Barber et al [2006] and Quigley et al [2005]. In Grocholsky et al [2011], Liang and Liang [2011] and others, geolocation algorithms for a network of aircraft are proposed.…”

Section: Introductionmentioning

confidence: 99%