A SLAM Pose Graph Optimization Method Using Dual Visual Odometry

Cao, Jianfei; Yu, Jincheng; Pan, Shangjie; Gao, Feng; Yu, Chao; Huang, Zhengfeng; Wang, Yu

doi:10.3724/sp.j.1089.2021.18663

Cited by 2 publications

(3 citation statements)

References 14 publications

(16 reference statements)

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“…The system attempts to learn through reinforcement learning by interacting with the environment and rewarding good behavior while penalizing undesirable behavior. In recent years, machine learning approaches [90] have gained popularity. These techniques include support vector machines [91,92], random forest, random [93] structured forest, and neural networks [94,95].…”

Section: The Role Of Machine Learning Algorithms Based On Vision For ...mentioning

confidence: 99%

“…In this manner, clusters are selected until the optimal ones can be ascertained [113]. Oliveira and Correia [90] proposed a method for crack identification and classification that is not dependent on the hand labeling of dataset pictures. A total of 84 road photos were acquired utilizing a digital camera as a means of training the system.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Azouz,

Honarvar Shakibaei Asli,

Khan

2023

Electronics

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Structural health monitoring (SHM) involves the control and analysis of mechanical systems to monitor the variation of geometric features of engineering structures. Damage processing is one of the issues that can be addressed by using several techniques derived from image processing. There are two types of SHM: contact-based and non-contact methods. Sensors, cameras, and accelerometers are examples of contact-based SHM, whereas photogrammetry, infrared thermography, and laser imaging are non-contact SHM techniques. In this research, our focus centres on image processing algorithms to identify the crack and analyze its properties to detect occurred damages. Based on the literature review, several preprocessing approaches were employed including image enhancement, image filtering to remove the noise and blur, and dynamic response measurement to predict the crack propagation.

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Section: The Role Of Machine Learning Algorithms Based On Vision For ...mentioning

confidence: 99%

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Azouz,

Honarvar Shakibaei Asli,

Khan

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…However, the disadvantages of laser sensors, such as the limited amount of acquired information and the bulky size, have restricted widespread application of laser SLAM. As a result, vSLAM, which is smaller, less expensive, and capable of acquiring richer information, has become an increasingly popular research di-rection in the field of SLAM (Cao et al, 2021;Huang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes

Chen,

Guo,

Qian

et al. 2024

Mech. Sci.

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Abstract. Identifying dynamic objects in dynamic scenes remains a challenge for traditional simultaneous localization and mapping (SLAM) algorithms. Additionally, these algorithms are not able to adequately inpaint the culling regions that result from excluding dynamic objects. In light of these challenges, this study proposes a novel visual SLAM (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes (VTD-SLAM), which leverages an improved Vision Transformer semantic segmentation technique to address these limitations. Specifically, VTD-SLAM utilizes a residual dual-pyramid backbone network to extract dynamic object region features and a multiclass feature transformer segmentation module to enhance the pixel weight of potential dynamic objects and to improve global semantic information for precise identification of potential dynamic objects. The method of multi-view geometry is applied to judge and remove the dynamic objects. Meanwhile, according to static information in the adjacent frames, the optimal nearest-neighbor pixel-matching method is applied to restore the static background, where the feature points are extracted for pose estimation. With validation in the public dataset TUM (The Entrepreneurial University Dataset) and real scenarios, the experimental results show that the root-mean-square error of the algorithm is reduced by 17.1 % compared with dynamic SLAM (DynaSLAM), which shows better map composition capability.

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A SLAM Pose Graph Optimization Method Using Dual Visual Odometry

Abstract: 曹剑飞 1) , 余金城 2) , 潘尚杰 1) , 高枫 2) , 于超 2) , 胥智林 2) , 黄正峰 1) , 汪玉 2)* 1) (合肥工业大学微电子学院合肥 230009) 2) (清华大学电子工程系北京 100084)

Cited by 2 publications

References 14 publications

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes

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A SLAM Pose Graph Optimization Method Using Dual Visual Odometry

Abstract: 曹剑飞 1) , 余金城 2) , 潘尚杰 1) , 高枫 2) , 于超 2) , 胥智林 2) , 黄正峰 1) , 汪玉 2)* 1) (合肥工业大学微电子学院 合肥 230009) 2) (清华大学电子工程系 北京 100084)

Cited by 2 publications

References 14 publications

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes

Contact Info

Product

Resources

About

Abstract: 曹剑飞 1) , 余金城 2) , 潘尚杰 1) , 高枫 2) , 于超 2) , 胥智林 2) , 黄正峰 1) , 汪玉 2)* 1) (合肥工业大学微电子学院合肥 230009) 2) (清华大学电子工程系北京 100084)