An improved visual SLAM based on affine transformation for ORB feature extraction

Cai, Lecai; Ye, Yuling; Gao, Xiang; Zhong, Li; Zhang, Chaoyang

doi:10.1016/j.ijleo.2020.165421

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…The core of indirect VSLAM is to detect, extract and match geometric features( points, lines, or planes), estimate camera pose, and build an environment map while retaining important information, it can effectively reduce calculation, so it has been widely used [67]. The VSLAM method based on point feature has long been taken into account as the mainstream method of indirect VSLAM due to its simplicity and practicality [68].…”

Section: Vslam Based On the Feature-based Methodsmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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Section: Vslam Based On the Feature-based Methodsmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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“…ORB is invariant to scale, rotation, and some affine changes. An alternative affine transformation-based ORB technique improves feature point extraction speed but may introduce redundant points and longer matching times [12,13].…”

Section: Orb (Oriented Fast and Rotated Brief)mentioning

confidence: 99%

Comparing Rotation-Robust Mechanisms in Local Feature Matching: Hand-Crafted vs. Deep Learning Algorithms

Rahman,

Lie,

Wahyudi

et al. 2024

Int. J. Innov. Mech. Eng. Adv. Mater

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The objective of this research is to conduct a performance comparison between hand-crafted feature matching algorithms and deep learning-based counterparts in the context of rotational variances. Hand-crafted algorithms underwent testing utilizing FLANN (Fast Library for Approximate Nearest Neighbors) as the matcher and RANSAC (Random sample consensus) for outlier detection and elimination, contributing to enhanced accuracy in the results. Surprisingly, experiments revealed that hand-crafted algorithms could yield comparable or superior results to deep learning-based algorithms when exposed to rotational variances. Notably, the application of horizontally flipped images showcased a distinct advantage for deep learning-based algorithms, demonstrating significantly improved results compared to their hand-crafted counterparts. While deep learning-based algorithms exhibit technological advancements, the study found that hand-crafted algorithms like AKAZE and AKAZE-SIFT could effectively compete with their deep learning counterparts, particularly in scenarios involving rotational variances. However, the same level of competitiveness was not observed in horizontally flipped cases, where hand-crafted algorithms exhibited suboptimal results. Conversely, deep learning algorithms such as DELF demonstrated superior results and accuracy in horizontally flipped scenarios. The research underscores that the choice between hand-crafted and deep learning-based algorithms depends on the specific use case. Hand-crafted algorithms exhibit competitiveness, especially in addressing rotational variances, while deep learning-based algorithms, exemplified by DELF, excel in scenarios involving horizontally flipped images, showcasing the unique advantages each approach holds in different contexts.

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“…The BRIEF(Binary Robust Independent Elementary Features) [10] descriptor is a binary descriptor whose arithmetic is：…”

Section: Calculation Of Descriptorsmentioning

confidence: 99%