Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Dianah Rose Abeho; Moreblessings Shoko; Patroba Achola Odera

Makale detayları

Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Yazarlar:

Dianah Rose Abeho | Moreblessings Shoko | Patroba Achola Odera

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Photogrametry

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 3, pp.314-323

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331630

pISSN:

2548-0960

Anahtar kelimeler:

Özet:

 The utilisation of Unmanned Aerial Vehicles UAV mounted with non-metric consumer-grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, accurate camera parameters must be determined through camera calibration. Camera calibration can be performed before pre-calibration or during the bundle block adjustment self-calibration. This study aims to analyse the effect of camera calibration parameters on the accuracy of UAV products, namely the Digital Elevation Model DEM and orthoimage. Camera calibration parameters are estimated using self-calibration, which deploys 3D image information of the scene in a bundle adjustment, and a 2D reference object-based approach known as Zhangs technique, which requires image information of a planar pattern. This study deployed a DJI FC220 camera mounted on a DJI Mavic Pro UAV. Self-calibration was deployed in Agisoft Metashape software based on Browns method, and Zhangs technique was deployed in MATLAB and OpenCV. Based on internal accuracy measures, OpenCV yields a minor reprojection error of 0.14, followed by MATLAB 0.79 and self-calibration 1.21. Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is, the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm for the horizontal and vertical, respectively, followed by Zhangs technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of the accuracy of UAV mapping products computed with and without calibration emphasises the need for camera calibration to optimise the accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

 The utilisation of Unmanned Aerial Vehicles UAV mounted with non-metric consumer-grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, accurate camera parameters must be determined through camera calibration. Camera calibration can be performed before pre-calibration or during the bundle block adjustment self-calibration. This study aims to analyse the effect of camera calibration parameters on the accuracy of UAV products, namely the Digital Elevation Model DEM and orthoimage. Camera calibration parameters are estimated using self-calibration, which deploys 3D image information of the scene in a bundle adjustment, and a 2D reference object-based approach known as Zhangs technique, which requires image information of a planar pattern. This study deployed a DJI FC220 camera mounted on a DJI Mavic Pro UAV. Self-calibration was deployed in Agisoft Metashape software based on Browns method, and Zhangs technique was deployed in MATLAB and OpenCV. Based on internal accuracy measures, OpenCV yields a minor reprojection error of 0.14, followed by MATLAB 0.79 and self-calibration 1.21. Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is, the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm for the horizontal and vertical, respectively, followed by Zhangs technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of the accuracy of UAV mapping products computed with and without calibration emphasises the need for camera calibration to optimise the accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

 The utilisation of Unmanned Aerial Vehicles UAV mounted with non-metric consumer-grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, accurate camera parameters must be determined through camera calibration. Camera calibration can be performed before pre-calibration or during the bundle block adjustment self-calibration. This study aims to analyse the effect of camera calibration parameters on the accuracy of UAV products, namely the Digital Elevation Model DEM and orthoimage. Camera calibration parameters are estimated using self-calibration, which deploys 3D image information of the scene in a bundle adjustment, and a 2D reference object-based approach known as Zhangs technique, which requires image information of a planar pattern. This study deployed a DJI FC220 camera mounted on a DJI Mavic Pro UAV. Self-calibration was deployed in Agisoft Metashape software based on Browns method, and Zhangs technique was deployed in MATLAB and OpenCV. Based on internal accuracy measures, OpenCV yields a minor reprojection error of 0.14, followed by MATLAB 0.79 and self-calibration 1.21. Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is, the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm for the horizontal and vertical, respectively, followed by Zhangs technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of the accuracy of UAV mapping products computed with and without calibration emphasises the need for camera calibration to optimise the accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

 The utilisation of Unmanned Aerial Vehicles UAV mounted with non-metric consumer-grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, accurate camera parameters must be determined through camera calibration. Camera calibration can be performed before pre-calibration or during the bundle block adjustment self-calibration. This study aims to analyse the effect of camera calibration parameters on the accuracy of UAV products, namely the Digital Elevation Model DEM and orthoimage. Camera calibration parameters are estimated using self-calibration, which deploys 3D image information of the scene in a bundle adjustment, and a 2D reference object-based approach known as Zhangs technique, which requires image information of a planar pattern. This study deployed a DJI FC220 camera mounted on a DJI Mavic Pro UAV. Self-calibration was deployed in Agisoft Metashape software based on Browns method, and Zhangs technique was deployed in MATLAB and OpenCV. Based on internal accuracy measures, OpenCV yields a minor reprojection error of 0.14, followed by MATLAB 0.79 and self-calibration 1.21. Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is, the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm for the horizontal and vertical, respectively, followed by Zhangs technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of the accuracy of UAV mapping products computed with and without calibration emphasises the need for camera calibration to optimise the accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

Makale detayları

Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Yazarlar:

Dianah Rose Abeho | Moreblessings Shoko | Patroba Achola Odera

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Photogrametry

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 3, pp.314-323

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331630

pISSN:

2548-0960

Camera calibration

Camera orientation parameters

UAV

DEM

Orthoimage

Sayı

Makale detayları

Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Yazarlar: Dianah Rose Abeho | Moreblessings Shoko | Patroba Achola Odera

Yayıncı: Murat YAKAR

Ana Başlıklar: Photogrametry

Dergi: International Journal of Engineering and Geosciences Vol.9 Issue 3, pp.314-323

Belge Tipi: Makale

Yayın Tarihi: 10/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001331630

pISSN: 2548-0960

Camera calibration

Camera orientation parameters

UAV

DEM

Orthoimage

Sayı

Yazarlar:

Dianah Rose Abeho | Moreblessings Shoko | Patroba Achola Odera

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Photogrametry

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 3, pp.314-323

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331630

pISSN:

2548-0960