Fully Automated Image Orientation in the Absence of Targets

Stamatopoulos, Christos; Chuang, Tzu Yi; Fraser, Clive S.; Lu, Yi

doi:10.5194/isprsarchives-xxxix-b5-303-2012

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…and r + j,i represents the imaging beam of the conjugate point in each image. When three images are available, the scale-invariant feature transform (SIFT) descriptor [28] is adopted to extract image features, and a cross-search strategy [29] along with a quasi-random sample consensus (RANSAC) [30] is applied to determine corresponding point triplets in the images. In cases that the number of triple correspondences is lower than a predefined threshold, the new image is ignored, and matching proceeds for the next newly collected image.…”

Section: Positioning In a Pipelinementioning

confidence: 99%

Learning and SLAM Based Decision Support Platform for Sewer Inspection

Chuang

Sung

2020

Remote Sensing

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Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.

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Section: Positioning In a Pipelinementioning

confidence: 99%

Learning and SLAM Based Decision Support Platform for Sewer Inspection

Chuang

Sung

2020

Remote Sensing

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“…Finally, all remaining points are considered as correct point matches. More details can be found in (Stamatopoulos et al, 2012).…”

Section: Extracting and Matching Pointsmentioning

confidence: 99%

Relative Pose Estimation Using Image Feature Triplets

Chuang¹,

Rottensteiner²,

Heipke³

2015

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

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ABSTRACT:A fully automated reconstruction of the trajectory of image sequences using point correspondences is turning into a routine practice. However, there are cases in which point features are hardly detectable, cannot be localized in a stable distribution, and consequently lead to an insufficient pose estimation. This paper presents a triplet-wise scheme for calibrated relative pose estimation from image point and line triplets, and investigates the effectiveness of the feature integration upon the relative pose estimation. To this end, we employ an existing point matching technique and propose a method for line triplet matching in which the relative poses are resolved during the matching procedure. The line matching method aims at establishing hypotheses about potential minimal line matches that can be used for determining the parameters of relative orientation (pose estimation) of two images with respect to the reference one; then, quantifying the agreement using the estimated orientation parameters. Rather than randomly choosing the line candidates in the matching process, we generate an associated lookup table to guide the selection of potential line matches. In addition, we integrate the homologous point and line triplets into a common adjustment procedure. In order to be able to also work with image sequences the adjustment is formulated in an incremental manner. The proposed scheme is evaluated with both synthetic and real datasets, demonstrating its satisfactory performance and revealing the effectiveness of image feature integration.

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“…These mismatches need to be eliminated because, firstly, they will result in erroneous 3D points and in the absence of additional imaging rays they will not be detectable as blunders in later network orientations. Secondly, the complexity of subsequent procedures is reduced when erroneous matches are eliminated (Stamatopoulos et al, 2012). Within the developed filtering/refinement procedure, the feature points of an image are clustered using a theoretical grid.…”

Section: Epipolar Mismatch Filteringmentioning

confidence: 99%

“…The kd-tree is a highly scalable algorithm and the availability of each extra CPU core can halve the search time. Alternatively, a NN search running on the GPU can be used, as proposed by Stamatopoulos et al (2012).…”

Section: Initial Point Correspondence Determinationmentioning

confidence: 99%

Automated Target-Free Network Orienation and Camera Calibration

Stamatopoulos

Fraser

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Automated close-range photogrammetric network orientation and camera calibration has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO) and subsequent spatial resection of the images. However, over the last decade, advances coming mainly from the computer vision (CV) community have allowed for fully automated orientation via feature-based matching techniques. There are a number of advantages in such methodologies for various types of applications, as well as for cases where the use of artificial targets might be not possible or preferable, for example when attempting calibration from low-level aerial imagery, as with UAVs, or when calibrating long-focal length lenses where small image scales call for inconveniently large coded targets. While there are now a number of CV-based algorithms for multi-image orientation within narrow-baseline networks, with accompanying open-source software, from a photogrammetric standpoint the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown. The objective addressed in this paper is target-free automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. The focus is on both the development of a methodology that overcomes the shortcomings that can be present in current CV algorithms, and on the photogrammetric priorities and requirements that exist in current processing pipelines. This paper also reports on the application of the proposed methodology to automated target-free camera self-calibration and discusses the process via practical examples.

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Fully Automated Image Orientation in the Absence of Targets

Cited by 10 publications

References 8 publications

Learning and SLAM Based Decision Support Platform for Sewer Inspection

Learning and SLAM Based Decision Support Platform for Sewer Inspection

Relative Pose Estimation Using Image Feature Triplets

Automated Target-Free Network Orienation and Camera Calibration

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