Camera Calibration Using Multiple Unordered Coplanar Chessboards

Γραμματικόπουλος, Λάζαρος; Adam, Kenneth R.; Petsa, E.; Karras, George C.

doi:10.5194/isprs-archives-xlii-2-w18-59-2019

Cited by 10 publications

(13 citation statements)

References 56 publications

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“…We believe that the application in this study can serve as a proof‐of‐concept application for the use of thermal sensors, but its success also suggests that the approach can be applied to other sensors—for example, multispectral and hyperspectral (e.g., Lucieer et al., 2012; Maes et al., 2017). Furthermore, we want to highlight that the method is not limited to SfM photogrammetry but can be used for other applications that require pre‐calibration—for example, image rectification (Eltner et al., 2021; Grammatikopoulos et al., 2019) or image fusion to generate thermal point clouds (Javadnejad et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

Senn

Mills

Walsh

et al. 2022

Earth Surf Processes Landf

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The application of structure from motion (SfM) photogrammetry for digital elevation model (DEM) and orthophoto generation from visible imagery enjoys ever-growing popularity in geomorphological research. Photogrammetry experts, however, urge that a rigorous approach is a prerequisite for reliable results-a requirement that may conflict with real-world survey. We present a method that unites the two disciplines, using the example of a challenging SfM photogrammetric survey at a Scottish river.Using simultaneous geometric pre-calibration of a multi-sensor remotely piloted aircraft system (RPAS), the method facilitates time-efficient topography mapping and the integration of other wavelengths to create orthophotos providing additional surface information. The approach utilizes an on-site 3D structure-for example, a building, as calibration object, by extracting coordinates of natural features from lidar scans and sensor imagery. We assess the workflow with specialized calibration software (VMS) and widely applied commercial SfM photogrammetric software (AM), using a DJI Phantom optical and a Workswell thermal sensor. We achieved calibration accuracies below one-third (optical) and one-quarter (thermal) of a pixel. Subsequently, we transfer the sensor parameters to pre-calibrate the SfM application and compare the results to a self-calibrated workflow. In a systematic experiment using the optical river survey dataset, we assess the effectiveness of pre-calibration, oblique imagery, scale variation and masking to mitigate systematic DEM errors.Opposing trends show between the calibration strategies. Decreasing network complexity (i.e., flying heights/view angles) improves pre-calibrated but compromises self-calibrated scenarios. Pre-calibrating (VMS) imagery from a single height (30 m nadir) yielded the best results. This finding could have implications for geomorphological surveys, in which single-scale datasets are widespread practice, despite the literature's urge towards more complex imaging networks. The self-calibrated results legitimise this insistence: The same dataset resulted in pronounced dome-shaped DEM distortion, indicating systematic errors, whereas additional flying heights and angles significantly improved the results.

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Section: Discussionmentioning

confidence: 99%

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

Senn

Mills

Walsh

et al. 2022

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…The proposed approach is based on the establishment of dense and highly accurate pairs of corresponding points among Lidar scans and RGB images, aiming at the recovery of the exterior orientation parameters (exterior calibration) of the camera with respect to the Lidar sensor. The internal parameters of the camera were acquired first by exploiting open-source camera calibration toolboxes available in the computer vision community (e.g., OpenCV [ 22 ], faucal toolbox [ 23 ], and UAV-based calibration [ 24 ]). The outline of the geometrical calibration is presented in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Such approaches take advantage of a single and typical planar chessboard, observed by the camera from different orientations and relatively small distances. For longer imaging distances, a multi-chessboard calibration scheme may also be adopted [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

An Effective Camera-to-Lidar Spatiotemporal Calibration Based on a Simple Calibration Target

Γραμματικόπουλος

Papanagnou²,

Venianakis³

et al. 2022

Sensors

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In this contribution, we present a simple and intuitive approach for estimating the exterior (geometrical) calibration of a Lidar instrument with respect to a camera as well as their synchronization shifting (temporal calibration) during data acquisition. For the geometrical calibration, the 3D rigid transformation of the camera system was estimated with respect to the Lidar frame on the basis of the establishment of 2D to 3D point correspondences. The 2D points were automatically extracted on images by exploiting an AprilTag fiducial marker, while the detection of the corresponding Lidar points was carried out by estimating the center of a custom-made retroreflective target. Both AprilTag and Lidar reflective targets were attached to a planar board (calibration object) following an easy-to-implement set-up, which yielded high accuracy in the determination of the center of the calibration target. After the geometrical calibration procedure, the temporal calibration was carried out by matching the position of the AprilTag to the corresponding Lidar target (after being projected onto the image frame), during the recording of a steadily moving calibration target. Our calibration framework was given as an open-source software implemented in the ROS platform. We have applied our method to the calibration of a four-camera mobile mapping system (MMS) with respect to an integrated Velodyne Lidar sensor and evaluated it against a state-of-the-art chessboard-based method. Although our method was a single-camera-to-Lidar calibration approach, the consecutive calibration of all four cameras with respect to the Lidar sensor yielded highly accurate results, which were exploited in a multi-camera texturing scheme of city point clouds.

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“…Recently, Unmanned Aerial Vehicles (UAVs) have been used to generate DEMs based on an automatic photogrammetric methodology, but although they excel in efficiency and ease, they impose several technical restrictions concerning the image capturing process. These constraints include the need for sophisticated equipment, the precise planning of the capturing mission, the specialized and laborious calibration operation [16], while, simultaneously, many factors have to be taken into consideration regarding the planning and the processing pipeline, such as image overlap, flight altitude, camera and lens characteristics among others [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning

et al. 2020

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Generating Digital Elevation Models (DEM) from satellite imagery or other data sources constitutes an essential tool for a plethora of applications and disciplines, ranging from 3D flight planning and simulation, autonomous driving and satellite navigation, such as GPS, to modeling water flow, precision farming and forestry. The task of extracting this 3D geometry from a given surface hitherto requires a combination of appropriately collected corresponding samples and/or specialized equipment, as inferring the elevation from single image data is out of reach for contemporary approaches. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have experienced unprecedented growth in recent years as they can extrapolate rules in a data-driven manner and retrieve convoluted, nonlinear one-to-one mappings, such as an approximate mapping from satellite imagery to DEMs. Therefore, we propose an end-to-end Deep Learning (DL) approach to construct this mapping and to generate an absolute or relative point cloud estimation of a DEM given a single RGB satellite (Sentinel-2 imagery in this work) or drone image. The model has been readily extended to incorporate available information from the non-visible electromagnetic spectrum. Unlike existing methods, we only exploit one image for the production of the elevation data, rendering our approach less restrictive and constrained, but suboptimal compared to them at the same time. Moreover, recent advances in software and hardware allow us to make the inference and the generation extremely fast, even on moderate hardware. We deploy Conditional Generative Adversarial networks (CGAN), which are the state-of-the-art approach to image-to-image translation. We expect our work to serve as a springboard for further development in this field and to foster the integration of such methods in the process of generating, updating and analyzing DEMs.

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Camera Calibration Using Multiple Unordered Coplanar Chessboards

Cited by 10 publications

References 56 publications

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

An Effective Camera-to-Lidar Spatiotemporal Calibration Based on a Simple Calibration Target

Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning

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