Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

Helgesen, Håkon Hagen; Leira, Frederik Stendahl; Fossen, Thor I.; Johansen, Tor Arne

doi:10.1007/s10846-017-0722-3

Cited by 17 publications

(14 citation statements)

References 38 publications

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“…Georeferencing will not be described in further detail since it is described more closely in several other articles (Leira et al 2015;Helgesen et al 2017;Rehak et al 2013;Hemerly 2014). However, a few important considerations and challenges need to be addressed.…”

Section: On-board Target Recognition and Position Estimationmentioning

confidence: 99%

“…Real-time image processing based on colour segmentation is used to detect the target and find the pixel position in the image. Georeferencing (Leira et al 2015;Helgesen et al 2017) is used to transform pixel coordinates from a single image into more informative Earth-fixed coordinates. Because georeferenced coordinates based on a single observation (image) can be uncertain, the mean georeferenced position, reconstructed from a batch of images, is used as the position of the target.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

et al. 2020

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Autonomous airdrop is a useful basic operation for a fixed-wing unmanned aerial system. Being able to deliver an object to a known target position extends operational range without risking human lives, but is still limited to known delivery locations. If the fixed-wing unmanned aerial vehicle delivering the object could also recognize its target, the system would take one step further in the direction of autonomy. This paper presents a closed-loop autonomous delivery system that uses machine vision to identify a target marked with a distinct colour, calculates the geographical coordinates of the target location and plans a path to a release point, where it delivers the object. Experimental results present a visual target estimator with a mean error distance of 3.4 m and objects delivered with a mean error distance of 5.5 m.

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Section: On-board Target Recognition and Position Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

et al. 2020

Self Cite

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“…Target tracking using UAVs equipped with optical sensors has been studied previously [12,13,16,17,22,30]. It is desirable to obtain high accuracy while maintaining filter consistency.…”

Section: Introductionmentioning

confidence: 99%

Camera-Based Tracking of Floating Objects using Fixed-wing UAVs

Helgesen

Bryne

Wilthil

et al. 2021

J Intell Robot Syst

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This article concerns tracking of floating objects using fixed-wing UAVs with a monocular thermal camera. Target tracking from an agile aerial vehicle is challenging because uncertainty in the UAV pose negatively affects the accuracy of the measurements obtained through thermal images. Consequently, the accuracy of the tracking estimates is degraded if navigation uncertainty is neglected. This is especially relevant for the estimated target covariance since inconsistency is a likely consequence. A tracking system based on the Schmidt-Kalman filter is proposed to mitigate navigation uncertainty. Images gathered with an uncertain UAV pose are weighted less than images captured with a reliable pose. The UAV pose is estimated independently in a multiplicative extended Kalman filter where the estimated covariance matrix is a measure of the uncertainty. The method is compared experimentally with two traditional alternatives based on the extended Kalman filter. The results show that the proposed method performs better with respect to consistency and accuracy.

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“…This includes topics such as navigation [4]- [6], navigation in GPS-denied environments [7], [8], collision avoidance [9] and autonomous take-off and landing [10]. From the application-based point of view, popular topics are target tracking [11], [12], inspection [13] and agriculture [14].…”

Section: Introductionmentioning

confidence: 99%

Colored-Noise Tracking of Floating Objects using UAVs with Thermal Cameras

Helgesen

Leira

Johansen

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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Tracking of floating objects using a fixed-wing UAV equipped with a thermal camera requires precise knowledge about the position and attitude of the UAV. Errors in the navigation estimates reduce the accuracy of the tracking system. Navigation errors are usually correlated in time and can propagate colored noise into the tracking filter. This work analyzes two approaches that seek to mitigate colored noise and they are compared experimentally with a third approach which assumes that the noise in the tracking system is purely white. Two independent flight experiments have been carried out where a small marine vessel was used as target. Thermal images of the target were captured and the position and velocity of the target have been estimated in an Earth-fixed coordinate system only using the images. The results show that the tracking filters perform well when considering the estimation error, but that colored noise should be accounted for to maintain filter consistency.

show abstract

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

Cited by 17 publications

References 38 publications

Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

Camera-Based Tracking of Floating Objects using Fixed-wing UAVs

Colored-Noise Tracking of Floating Objects using UAVs with Thermal Cameras

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