Vision-Based Target Three-Dimensional Geolocation Using Unmanned Aerial Vehicles

Zhang, Lele; Deng, Fang; Chen, Jie; Bi, Yingcai; Phang, Swee King; Chen, Xudong; Chen, Ben M.

doi:10.1109/tie.2018.2807401

Cited by 48 publications

(25 citation statements)

References 28 publications

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“…Zhang et al [ 23 ] use a stereo-vision technique to determine the target relative height with respect to the UAV. In addition, the sensor technology used in UAVs is very similar to that used in today’s smartphones [ 35 ].…”

Section: Georeferencing Methods For Uav Observationsmentioning

confidence: 99%

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Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter

Santana

Cherif

Bernardino

et al. 2022

Sensors

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Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory study on rough and flat terrains was conducted to use and validate the Iterative Ray-Tracing method in combination with a Bearings-Range Extended Kalman Filter as a real-time forest fire georeferencing and filtering algorithm on images captured by an aerial vehicle. The Iterative Ray-Tracing method requires a vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a calibrated camera, and a Digital Elevation Map (DEM). The proposed method receives the real-time input of the GPS, IMU, and the image coordinates of the pixels to georeference (computed by a companion algorithm of fire front detection) and outputs the geographical coordinates corresponding to those pixels. The Unscented Transform B is proposed to characterize the Iterative Ray-Tracing uncertainty. A Bearings-Range filter measurement model is introduced in a sequential filtering architecture to reduce the noise in the measurements, assuming static targets. A performance comparison is done between the Bearings-Only and the Bearings-Range observation models, and between the Extended and Cubature Kalman Filters. In simulation studies with ground truth, without filtering we obtained a georeferencing Root Mean Squared Errors (RMSE) of 30.7 and 43.4 m for the rough and flat terrains respectively, while filtering with the proposed Bearings-Range Extended Kalman Filter showed the best results by reducing the previous RMSE to 11.7 and 19.8 m, respectively. In addition, the comparison of both filter algorithms showed a good performance of Bearings-Range filter which was slightly faster. Indeed, these experiments based on the real data conducted to results demonstrated the applicability of the proposed methodology for the real-time georeferencing forest fires.

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Section: Georeferencing Methods For Uav Observationsmentioning

confidence: 99%

“…Zhang et al [ 22 ] proposed a Bearings-Only model with a CKF for relative spacecraft attitude and orientation estimation. Later, Zhang et al [ 23 ] presents a target geolocation method for UAV’s where range measurements are obtained from stereo vision but did not use a filtering.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter

Santana

Cherif

Bernardino

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…How to recognize the target in a spatial dimension is important [ 13 , 14 , 15 , 16 ]. Combined with the low-quality attitude-heading reference system (AHRS) on UAVs, Zhang et al [ 17 ] propose a new vision-based positioning method to determine the three-dimensional position of the target. The usual positioning method must rely on many requirements (i.e., geo-referenced terrain database, precise attitude sensor).…”

Section: Related Workmentioning

confidence: 99%

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs

Yuan

Ota

Dong

et al. 2022

Sensors

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Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.

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“…Moreover, a thumb positioning method determined by UGV is introduced, which makes the prediction for the geographical location in the detection. Combined with the low-quality attitude-heading reference system (AHRS) on UAVs, Zhang et al [6] propose a new vision-based positioning method to determine the three-dimensional position of the target. The usual positioning method must rely on many requirements (ie geo-referenced terrain database, precise attitude sensor).…”

Section: Scene Understanding For Uavsmentioning

confidence: 99%

Scan From the Sky: A Path Planning Method with Perception Optimization for UAVs

Yuan¹,

Ota²,

Zhao³

2021

Preprint

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Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provided is extremelyvaluable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by theon-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases,UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the flypath task for UAVs, considering the actual perception needs. A new convolutional neural network (CNN) model is proposed to detectand localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accutately recognizeas many as possible objects with a minimum time cost. The simulation results demonstrate that the proposed method is effective andefficient, and can well address the actual scene understanding and path planning problems for UAVs in the real world.

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Vision-Based Target Three-Dimensional Geolocation Using Unmanned Aerial Vehicles

Cited by 48 publications

References 28 publications

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs

Scan From the Sky: A Path Planning Method with Perception Optimization for UAVs

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