Journal ArticleDOI
An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency
Lilian Zhang,Reinhard Koch +1 more
TLDR
A line matching algorithm which utilizes both the local appearance of lines and their geometric attributes to solve the problem of segment fragmentation and geometric variation and is accurate even for low-texture images because of the pairwise geometric consistency evaluation.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2013-10-01. It has received 375 citations till now. The article focuses on the topics: Line segment & Matching (graph theory).read more
Citations
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Proceedings ArticleDOI
PL-SLAM: Real-time monocular visual SLAM with points and lines
TL;DR: This paper builds upon ORB-SLAM, presumably the current state-of-the-art solution both in terms of accuracy as efficiency, and extends its formulation to simultaneously handle both point and line correspondences, and demonstrates that the use of lines does not only improve the performance of the original ORB -SLAM solution in poorly textured frames, but also systematically improves it in sequence frames combining points and lines, without compromising the efficiency.
Journal ArticleDOI
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments
Ruben Gomez-Ojeda,Francisco-Angel Moreno,David Zuñiga-Noël,Davide Scaramuzza,Javier Gonzalez-Jimenez +4 more
TL;DR: PL-SLAM is proposed, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image.
Journal ArticleDOI
PL-VIO: Tightly-Coupled Monocular Visual-Inertial Odometry Using Point and Line Features.
TL;DR: The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.
Proceedings ArticleDOI
Robust visual SLAM with point and line features
TL;DR: The orthonormal representation is employed as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution.
Journal ArticleDOI
Indoor positioning and wayfinding systems: a survey
TL;DR: This article reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation, and investigates and contrasts the different navigation systems in each category.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI
ORB: An efficient alternative to SIFT or SURF
TL;DR: This paper proposes a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise, and demonstrates through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations.