Using aerial images to calibrate the inertial sensors of a low-cost multispectral autonomous remote sensing platform (AggieAir)

Jensen, Austin M.; Han, Yiding; Chen, YangQuan

doi:10.1109/igarss.2009.5418142

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…This is done by inverse orthorectifing the images to find the actual position and attitude of the UAV using ground references setup in a square. Actual data from a test flight is used to validate this method (Jensen, Han & Chen, 2009). As detailed above, a point in the image plane ( p i ) can be transformed into Earth-Centered Earth-Fixed (ECEF) coordinates ( p w ) using equation 13 where u w is the position of the UAV in ECEF, R c b is the rotation matrix from the camera frame to the body frame, R b n is the rotation matrix from the body frame to the navigation frame, R n w is the rotation matrix from the navigation frame to ECEF, and h is the height above ground of the UAV.…”

Section: Image Orthorectificationmentioning

confidence: 99%

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

Huo

Jensen²,

Han

et al. 2009

Advances in Geoscience and Remote Sensing

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Section: Image Orthorectificationmentioning

confidence: 99%

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

Huo

Jensen²,

Han

et al. 2009

Advances in Geoscience and Remote Sensing

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“…The method is also called the inverse georeferencing. 13,39 Finding errors and calibrating the camera result in more accurate IMU/GPS data, which obviously benefit both direct and indirect georeferencing methods to achieve better performance.…”

Section: Update the Inertial Measurement Unit/global Positioning Syst...mentioning

confidence: 99%

“…Direct georeferencing methods use the respective IMU/GPS data such as pitch, roll, yaw, altitude, latitude, and longitude to georeference each aerial image. [12][13][14] They are easy, fast, straightforward, and automatic. 2 They first perform a series of transformations to project four corners of each image to the earth coordinate and then create a three-dimensional surface and overlay images on the map using the terrestrial digital elevation model (DEM).…”

Section: Introductionmentioning

confidence: 99%

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Computer vision–based orthorectification and georeferencing of aerial image sets

Faraji

Jensen

2016

J. Appl. Remote Sens

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Generating a georeferenced mosaic map from unmanned aerial vehicle (UAV) imagery is a challenging task. Direct and indirect georeferencing methods may fail to generate an accurate mosaic map due to the erroneous exterior orientation parameters stored in the inertial measurement unit (IMU), erroneous global positioning system (GPS) data, and difficulty in locating ground control points (GCPs) or having a sufficient number of GCPs. This paper presents a practical framework to orthorectify and georeference aerial images using the robust features-based matching method. The proposed georeferencing process is fully automatic and does not require any GCPs. It is also a near real-time process which can be used to determine whether aerial images taken by UAV cover the entire target area. We also extend this framework to use the inverse georeferencing process to update the IMU/GPS data which can be further used to calibrate the camera of the UAV, reduce IMU/GPS errors, and thus produce more accurate mosaic maps by employing any georeferencing method. Our experiments demonstrate the effectiveness of the proposed framework in producing comparable mosaic maps as commercial software Agisoft and the effectiveness of the extended framework in significantly reducing the errors in the IMU/GPS data.

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“…However, the sensor error always varies with time, camera calibration too complex to calibrate camera before every flight. What's more, the best way to eliminate the error of aircraft sensor may be use the method of field calibration [14]. Therefore, this paper presents a method to obtain the prediction model of attitude errors with the results of field calibration in advance, which can effectively improve the positioning accuracy but also guarantee the instantaneity.…”

Section: Introductionmentioning

confidence: 99%

High Precision Target Localization Method Based On Compensation Of Attitude Angle Errors

Ding²,

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-The attitude angles of UAV, as the input parameters of the target localization process, influence the accuracy of geo-targeting. In order to improve the accuracy of target localization, this paper compensates the attitude angle errors of the UAV based on learning prediction compensation. Firstly, considering the airborne equipment and the metadata provided by the UAV, we combine rear intersection with GPS/INS to calculate the error of each platform and aircraft attitude angle. Then the error prediction model to compensate each platform and aircraft attitude error is derived by analyzing the error distribution and polynomial regression. Afterwards, because of the limit of the UAV aerial image amount and the similar influence of each attitude angle error on targeting and geometric correction, we use equivalent optical axis angle to represent platform and aircraft attitudes. Furthermore, we also predict and compensate the error of the equivalent angle. In this process, we adopt SVM and regression to classify and obtain error prediction model of equivalent optical axis angle. Finally, the actual data is used to verify the compensation algorithm. The results show that the method can improve the accuracy of target localization efficiently, and has a certain value of engineering guidance and practical application.

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Using aerial images to calibrate the inertial sensors of a low-cost multispectral autonomous remote sensing platform (AggieAir)

Cited by 7 publications

References 5 publications

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

Computer vision–based orthorectification and georeferencing of aerial image sets

High Precision Target Localization Method Based On Compensation Of Attitude Angle Errors

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