An Efficient Algebraic Solution to the Perspective-Three-Point Problem

Ke, Tong; Roumeliotis, Stergios I.

doi:10.1109/cvpr.2017.491

Cited by 113 publications

(71 citation statements)

References 20 publications

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“…Such matches are then exploited to obtain an initial estimate of camera extrinsic parameters (and possible camera intrinsics ). In cases in which the camera is calibrated, the solution is obtained by solving a minimal case ( = 3) of the Perspective-n-Point (PnP) problem (Ke and Roumeliotis, 2017), while alternatively a Direct Linear Transform (DLT) (Hartley and Zisserman, 2003) algorithm can be used. As feature observations are noisy and might contain outliers, the process is done in conjunction with a robust estimation method A Contrario Ransac (AC-RANSAC) (Moisan et al, 2012).…”

Section: Pose Estimationmentioning

confidence: 99%

Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios

Istenič

Gracias

Arnaubec

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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A B S T R A C TRecent advances in structure-from-motion techniques are enabling many scientific fields to benefit from the routine creation of detailed 3D models. However, for a large number of applications, only a single camera is available for the image acquisition, due to cost or space constraints in the survey platforms. Monocular structure-from-motion raises the issue of properly estimating the scale of the 3D models, in order to later use those models for metrology. The scale can be determined from the presence of visible objects of known dimensions, or from information on the magnitude of the camera motion provided by other sensors, such as GPS.This paper addresses the problem of accurately scaling 3D models created from monocular cameras in GPS-denied environments, such as in underwater applications. Motivated by the common availability of underwater laser scalers, we present two novel approaches which are suitable for different laser scaler configurations. A fully-calibrated method enables the use of arbitrary laser setups, while a partially-calibrated method reduces the need for calibration by only assuming parallelism on the laser beams, with no constraints on the camera. The proposed methods have several advantages with respect to the existing methods. By using the known geometry of the scene expressed by the 3D model, along with some parameters of the laser scaler geometry, the need for laser alignment with the optical axis of the camera is removed. Furthermore, the extremely error-prone manual identification of image points on the 3D model, currently required in image-scaling methods, is eliminated as well.The performance of the methods and their applicability was evaluated on both data generated from a realistic 3D model and data collected during an oceanographic cruise in 2017. Three separate laser configurations have been tested, encompassing nearly all possible laser setups, to evaluate the effects of terrain roughness, noise, camera perspective angle and camera-scene distance on the final estimates of scale. In the real scenario, the computation of 6 independent model scale estimates using our fullycalibrated approach, produced values with standard deviation of 0.3%. By comparing the values to the only possible method usable for this dataset, we showed that the consistency of scales obtained for individual lasers is much higher for our approach (0.6% compared to 4%).

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Section: Pose Estimationmentioning

confidence: 99%

Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios

Istenič

Gracias

Arnaubec

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

show abstract

“…RANSAC uses an inner algorithm to solve the perspective-3-point (P3P) problem that gives the camera pose based on a minimal set of three known feature points. We considered various P3P solving algorithms available in OpenCV and settled on AP3P (46) during initial testing.…”

Section: Matching and Pose Estimationmentioning

confidence: 99%

Synthetic photometric landmarks used for absolute navigation near an asteroid

2020

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The need for autonomous location estimation in the form of optical navigation is an essential requirement for forthcoming deep space missions. While crater-based navigation might work well with larger bodies littered with craters, small sub-kilometer bodies do not necessarily have them. We have developed a new pose estimation method for absolute navigation based on photometric local feature extraction techniques thus making it suitable for missions that cannot rely on craters. The algorithm can be used by a navigation filter in conjunction with relative pose estimation such as visual odometry for additional robustness and accuracy. To estimate the position and orientation of the spacecraft in the asteroid-fixed coordinate frame, it uses navigation camera images in combination with other readily available information, such as orientation relative to the stars and the current time for an initial estimate of the asteroid rotation state. Evaluation of the algorithm when using different feature extractors is performed, on one hand, using Monte Carlo simulations and, on the other hand, using actual images taken by the Rosetta spacecraft orbiting the comet 67P/Churyumov–Gerasimenko. Our analysis, where four different feature extraction methods (AKAZE, ORB, SIFT, SURF) were compared, showed that AKAZE is most promising in terms of stability and accuracy.

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“…Obviously, fewer are the key-points and their multiplicity, higher will be the uncertainty of the calibration and orientation computed with SR. Moreover, with both methods a result can converge to a false or odd solution for which some refinement methods will be prospected in a near future, like the incremental approaches of Open-MVG (Moulon et al, 2016, Ke andRoumeliotis, 2017), up-to-date perspective-three-point solver (Larsson et al, 2017, Persson andNordberg, 2018) or using 2D/3D Mutual-Information registration (Palma et al, 2010). However despite of current limitation, the outcome is encouraging for forthcoming extensions and modules of TACO to endorse its ability to process complex multimodal CH imaging data sets.…”

Section: A Versatile Incremental Spatial Registration Of New Images Omentioning

confidence: 99%

A Fully Automated Incremental Photogrammetric Processing Dedicated for Collaborative Remote-Computing Workflow

Pamart¹,

Morlet²,

Luca³

2019

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

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<p><strong>Abstract.</strong> Image based-modeling practices in the field of Cultural Heritage studies are nowadays no longer seen as one-shot applications but as various and complex multimodal scenarios. Current use of SFM and photogrammetric methods implies their extensions to facilitate the management of complex multimodal data sets carried-out by different experts around a single heritage asset. In order to fully benefit of collaborative semantic enrichment of spatially oriented resources, a versatile and robust solution have been developed to enable incremental registration of image-sets within the web-based platform AIOLI. For this purpose, this paper will present an on-going development of a Totally Automated Co-registration and Orientations (TACO) work-flow.</p>

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An Efficient Algebraic Solution to the Perspective-Three-Point Problem

Cited by 113 publications

References 20 publications

Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios

Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios

Synthetic photometric landmarks used for absolute navigation near an asteroid

A Fully Automated Incremental Photogrammetric Processing Dedicated for Collaborative Remote-Computing Workflow

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