Application of an Improved Fast Corner Detection Algorithm in ORB-SLAM2

Huo, Xiaochuang; Zhang, Lei; Liu, Guitao; Du, Zhi‐Zhi; Liu, Mingmin

doi:10.1007/978-981-16-9492-9_180

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…49 Among visual SLAM methods, ORB-SLAM2 is always utilized by many scholars as the backbone to build their SLAM systems because of its clear modules, rich interfaces, and easy development. [50][51][52][53][54] However, these methods are only applied to working scenes with normal weather conditions, as pixel occlusion, texture blur, and feature instability caused by image degradation impose significant pose errors. Some researchers propose calculating the poses between rain or haze images to improve the SLAM system's robustness.…”

Section: Visual Slammentioning

confidence: 99%

“…ORB-SLAM2 46 is often used as the backbone of many visual SLAM architectures due to its clear modules, rich interfaces, and easy development. [50][51][52][53][54] Thus, the ORB-SLAM2 is chosen as the basic architecture of the visual SLAM system for multiple bad weather conditions. The weather persistence assumption is put forward considering the persistence of weather events in the real world.…”

Section: Overview Of the Slam Frameworkmentioning

confidence: 99%

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Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

Dong

Guo

et al. 2023

Opt. Eng.

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Image degradation caused by bad weather, such as haze, rain, and snow, significantly degrades a vision system's performance, imposing challenges to the autonomous motion of mobile robots. Therefore, it is crucial to restore the degraded images for mobile robots operating outdoors. Spurred by this concern, a framework that restores degraded images and affords simultaneous localization and mapping (SLAM) under multiple bad weather conditions is presented. Specifically, the developed architecture combines the advantages of convolutional neural network features and weather features. It improves the accuracy of identifying the degradation type by developing a weather inference module based on ensemble learning. According to the internal mechanism of image degradation, a degraded image restoration method is proposed utilizing physical models, with a subsequent operation refining the preliminary restored results. To improve our method's adaptability to real scenes, unpaired real-world weather images are introduced into the degradation removal algorithm through generative adversarial networks. The proposed weather persistent assumption combines weather inference and degradation restoration modules in the SLAM system to capture multiple weather conditions, which improves the system's accuracy and running speed. Comprehensive experiments evaluating the developed framework and its components highlight that the proposed weather inference and degraded image restoration methods achieve a highly appealing effect. The final experimental results demonstrate that our framework autonomously identifies weather types, triggers the corresponding restoration, and realizes accurate localization in multiple bad weather conditions. It affords SLAM accuracy under multiple bad weather conditions that is close to raw SLAM in clear weather.

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Section: Visual Slammentioning

confidence: 99%

Section: Overview Of the Slam Frameworkmentioning

confidence: 99%

Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

Dong

Guo

et al. 2023

Opt. Eng.

View full text Add to dashboard Cite

show abstract

“…The traditional ORB [25] method has a large number of feature points aggregated in texture information-rich and motion scenes, and the overly concentrated features will reduce the available information of the image, which is not conducive to the subsequent work. Therefore, in this paper, an improved balanced quadtree method is used to manage the feature points so that they are uniformly distributed in the whole image and increase the usable information of the image.…”

Section: B Feature Extraction Experimentsmentioning

confidence: 99%

A Dense Visual SLAM Method in Dynamic Scenes

Liu,

Zhang,

Wang

et al. 2023

IEEE Access

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The visual SLAM algorithm has been developed to be more mature. It can achieve good performance in most static scenes, but when it is applied to dynamic scenes, it will be interfered with by a large number of dynamic points in the scene, which affects the system accuracy and even leads to tracking failures, and the sparse maps built by traditional SLAM algorithms can not satisfy advanced needs such as navigation. Aiming at the above problems, this paper proposes a dense visual SLAM method in dynamic scenes. In this paper, based on the ORB-SLAM2 algorithm, we use the improved balanced quadtree method to obtain uniformly distributed feature points, use the motion grid statistics method to improve the accuracy of feature matching between neighboring frames, add a new target detection network to obtain the a priori information of the dynamic objects, and then construct a dense map containing only static information in the dense map building thread. Finally, to verify the effectiveness of the algorithms in this paper, the TUM dataset is used to compare with the algorithms of ORB-SLAM2, ORB-SLAM3, DS-SLAM, and RDS-SLAM. The results show that this paper's SLAM algorithm improves 97.54%, 97.35%, 28.74%, and 17.37% on the root mean square error of the absolute trajectory error compared to the four algorithms in the measured highdynamic sequences, respectively.

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Application of an Improved Fast Corner Detection Algorithm in ORB-SLAM2

Cited by 2 publications

References 16 publications

Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

A Dense Visual SLAM Method in Dynamic Scenes

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