A New Line Matching Approach for High-Resolution Line Array Remote Sensing Images

Wang, Jingxue; Liu, Suyan; Zhang, Ping

doi:10.3390/rs14143287

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…The visual prior map based on 3D spatial straight lines is constructed under the framework of standard visual SLAM. LSD algorithm [16] and Line Band Descriptor (LBD) [17] are added to the front end of image feature point processing as the basic framework. At the same time, point and line features in the image are extracted.…”

Section: Visual Prior Map Assisted Mono Location Algorithm Based On 3...mentioning

confidence: 99%

Visual prior map assisted monocular location algorithm based on 3D spatial lines

Gong

Rao

Fan

et al. 2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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When the Visual-Inertial Odometry (VIO) is started, its Inertial Measurement Unit (IMU) lacks acceleration incentive, which will result in poor orientation estimation accuracy during initialization, or even initialization failure. Therefore, a visual priori map-assisted monocular location algorithm based on 3D spatial straight lines is proposed. Firstly, the monocular image data of the surrounding environment were extracted through the Line Segment Detection algorithm (LSD), and high precision 2D line features were selected according to the length of the line and the number of surrounding point features. The 3D spatial lines of the surrounding environment were obtained using the line and surface intersection method. Construct a visual prior map with 3D spatial straight lines. Secondly, the constructed visual prior map is used as the online monocular VIO pose estimation for the global map. Based on the straight-line feature matching algorithm and the 3D space straight line depth information as additional constraints, the 2D straight-line feature in the monocular VIO's current field of vision is matched with the 3D space straight line in the visual prior map. The matching results were used as global constraints to optimize the monocular VIO pose. Tests on EUROC and TUM common data sets show that the 3D spatial straight line based visual prior map can effectively correct the pose during the monocular VIO initialization stage. Compared with the VINS-Mono localization algorithm, this algorithm can effectively improve the pose estimation accuracy during VIO initialization and reduce the overall trajectory positioning error.

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Section: Visual Prior Map Assisted Mono Location Algorithm Based On 3...mentioning

confidence: 99%

Visual prior map assisted monocular location algorithm based on 3D spatial lines

Gong

Rao

Fan

et al. 2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

View full text Add to dashboard Cite

show abstract

“…Therefore, the number of matching candidates will largely decrease. Third, most of the introduced multispectral line matching techniques assume that the transformation between two images can be described by a single homography [44], which indicates the existence of a global transformation. However, in man-made scenarios, multiple local homographies usually exist (which means the existence of multiple planes).…”

Section: Multispectral Feature Matchingmentioning

confidence: 99%

A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies

Yang

et al. 2022

Remote Sensing

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Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.

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3D building similarity for a random single-view-image pair based on a local 3D shape

Ying,

Zhang,

Wang

et al. 2023

International Journal of Applied Earth Observation and Geoinfor

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A New Line Matching Approach for High-Resolution Line Array Remote Sensing Images

Cited by 4 publications

References 35 publications

Visual prior map assisted monocular location algorithm based on 3D spatial lines

Visual prior map assisted monocular location algorithm based on 3D spatial lines

A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies

3D building similarity for a random single-view-image pair based on a local 3D shape

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