Sensor modeling, self-calibration and accuracy testing of panoramic cameras and laser scanners

Parian, Jafar Amiri; Gruen, Armin

doi:10.1016/j.isprsjprs.2009.08.005

Cited by 37 publications

(13 citation statements)

References 9 publications

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“…Previous calibration attempts have modelled the scanner based on the panoramic camera model [16], theodolites [17] and total stations [18,19]. The two former approaches are restricted to modelling systematic errors in the angular measurements and only the last method can simultaneously account for systematic errors in distances and directions.…”

Section: Previous Workmentioning

confidence: 99%

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Chow

Lichti

Glennie

et al. 2013

Sensors

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Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation) are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.

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Section: Previous Workmentioning

confidence: 99%

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Chow

Lichti

Glennie

et al. 2013

Sensors

View full text Add to dashboard Cite

show abstract

“…Ricoh Tetha, Samsung Gear 360, LG360 cam, Nikon Key Mission 360, see Figure 1), panoramic photography and 3D modelling involved (i) stitching shots taken at different viewpoints into one single frame (Fangi, 2007;Fangi, 2009) or (ii) the use of very expensive rotating linear array cameras (Schneider and Maas, 2003;Amiri Parian and Gruen, 2010). The former approach needs precise acquisition and stitching procedures, that are often computationally and time consuming and do not always guarantee the final quality of the photogrammetric restitution due to various deficiencies which characterize the whole process (Szeliski and Shum, 1997;Sakharkar and Gupta, 2013).…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Panoramic Camera for the 3d Documentation Of Contaminated Crime Scenes

Abate

Toschi

Colls

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Crime scene documentation is a fundamental task which has to be undertaken in a fast, accurate and reliable way, highlighting evidence which can be further used for ensuring justice for victims and for guaranteeing the successful prosecution of perpetrators. The main focus of this paper is on the documentation of a typical crime scene and on the rapid recording of any possible contamination that could have influenced its original appearance. A 3D reconstruction of the environment is first generated by processing panoramas acquired with the low-cost Ricoh Theta 360 camera, and further analysed to highlight potentials and limits of this emerging and consumer-grade technology. Then, a methodology is proposed for the rapid recording of changes occurring between the original and the contaminated crime scene. The approach is based on an automatic 3D feature-based data registration, followed by a cloud-to-cloud distance computation, given as input the 3D point clouds generated before and after e.g. the misplacement of evidence. All the algorithms adopted for panoramas pre-processing, photogrammetric 3D reconstruction, 3D geometry registration and analysis, are presented and currently available in open-source or low-cost software solutions.

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“…This method provides a general network around the object using central perspective images with normal focal lens and reconstructs "sharp" details through gnomonic projections. Spherical panoramas have also been employed to extract 3D information [25,[33][34][35]. G. Fangi [33] captured panoramas with the use of both normal and long focal lenses.…”

Section: Introductionmentioning

confidence: 99%

Scanning Photogrammetry for Measuring Large Targets in Close Range

et al. 2015

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Abstract:In close-range photogrammetry, images are difficult to acquire and organize primarily because of the limited field of view (FOV) of digital cameras when long focal lenses are used to measure large targets. To overcome this problem, we apply a scanning photography method that acquires images by rotating the camera in both horizontal and vertical directions at one station. This approach not only enlarges the FOV of each station but also ensures that all stations are distributed in order without coverage gap. We also conduct a modified triangulation according to the traits of the data overlapping among images from the same station to avoid matching all images with one another. This algorithm synthesizes the images acquired from the same station into synthetic images, which are then used to generate a free network. Consequently, we solve the exterior orientation elements of each original camera image in the free network and perform image matching among original images to obtain tie points. Finally, all original images are combined in self-calibration bundle adjustment with control points. The feasibility and precision of the proposed method are validated by testing it on two fields using 300 and 600 mm lenses. The results confirm that even with a small amount of control points, the developed scanning photogrammetry can steadily achieve millimeter scale accuracy at distances ranging from 40 m to 250 m.

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Sensor modeling, self-calibration and accuracy testing of panoramic cameras and laser scanners

Cited by 37 publications

References 9 publications

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

A Low-Cost Panoramic Camera for the 3d Documentation Of Contaminated Crime Scenes

Scanning Photogrammetry for Measuring Large Targets in Close Range

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