Monocular SLAM Initialization Using Epipolar and Homography Model

Butt, Maaz Mohammed; Zhang, Hai; Qiu, Xiaochen; Ge, Baoshuang

doi:10.1109/iccre49379.2020.9096497

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…This is necessary because the planar homography contains additional geometric information, which aids in reducing the uncertainty in determining the depth of pixel projection based on epipolar geometry [ 11 , 16 ]. In [ 34 ], an automatic real-time initialization module is implemented based on heuristic algorithms, which utilizes epipolar and homography geometry for the stable initialization of non-planar and planar scenes. All types of frameworks require reliable initialization to ensure optimal system performance [ 35 ], but as far as we know, there are no monocular initialization algorithms using structural constraints.…”

Section: Related Workmentioning

confidence: 99%

Accurate Monocular SLAM Initialization via Structural Line Tracking

Gu,

Zhang,

Liu

2023

Sensors

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In this paper, we present a novel monocular simultaneous localization and mapping (SLAM) initialization algorithm that relies on structural features by tracking structural lines. This approach addresses the limitations of the traditional method, which can fail to account for a lack of features or their uneven distribution. Our proposed method utilizes a sliding window approach to guarantee the quality and stability of the initial pose estimation. We incorporate multiple geometric constraints, orthogonal dominant directions, and coplanar structural lines to construct an efficient pose optimization strategy. Experimental evaluations conducted on both the collected chessboard datasets and real scene datasets show that our approach provides superior results in terms of accuracy and real-time performance compared to the well-tuned baseline methods. Notably, our algorithm achieves these improvements while being computationally lightweight, without the need for matrix decomposition.

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Section: Related Workmentioning

confidence: 99%

Accurate Monocular SLAM Initialization via Structural Line Tracking

Gu,

Zhang,

Liu

2023

Sensors

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“…Furthermore, homography estimation has shown irreplaceable importance in developing Simultaneous Localization and Mapping (SLAM) [129,130] techniques, especially in indoor environments without GPS support. It plays a central role in environmental mapping and path planning and provides critical visual information for automatic navigation systems.…”

Section: Applications Fieldsmentioning

confidence: 99%

A Review of Homography Estimation: Advances and Challenges

Luo,

Wang,

Liao

et al. 2023

Electronics

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Images captured from different viewpoints or devices have often exhibited significant geometric and photometric differences due to factors such as environmental variations, camera technology differences, and shooting conditions’ instability. To address this problem, homography estimation has attracted much attention as a method to describe the geometric projection relationship between images. Researchers have proposed numerous homography estimation methods for single-source and multimodal images in the past decades. However, the comprehensive review and analysis of homography estimation methods, from feature-based to deep learning-based, is still lacking. Therefore, we provide a comprehensive overview of research advances in homography estimation methods. First, we provide a detailed introduction to homography estimation’s core principles and matrix representations. Then, we review homography estimation methods for single-source and multimodal images, from feature-based to deep learning-based methods. Specifically, we analyze traditional and learning-based methods for feature-based homography estimation methods in detail. For deep learning-based homography estimation methods, we explore supervised, unsupervised, and other methods in-depth. Subsequently, we specifically review several metrics used to evaluate these methods. After that, we analyze the relevant applications of homography estimation and show the broad application prospects of this technique. Finally, we discuss current challenges and future research directions, providing a reference for computer vision researchers and engineers.

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“…In general, Visual SLAM algorithms have three basic modules: initialization [38], tracking and mapping [39]. The initialization consists of defining the global coordinate system of the environment to be mapped, as well as the reconstruction of part of its elements, which will be used as a reference for the beginning of the tracking and mapping.…”

Section: Slam Algorithmsmentioning

confidence: 99%