Two-Stage vSLAM Loop Closure Detection Based on Sequence Node Matching and Semi-Semantic Autoencoder

Wang, Zhonghua; Peng, Zhen; Guan, Yong Liang; Wu, Lifeng

doi:10.1007/s10846-020-01302-0

Cited by 4 publications

(2 citation statements)

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“…The authors of [44] utilized a deep CNN to perform semantic segmentation and extract loop feature of images at the same time. In [45], an unsupervised semi-semantic auto-encoder model DeepLab_AE was designed to obtain semantic features of scenes. The authors of [46] proposed a place recognition method via re-identification of salient objects.…”

Section: Semantic-information-based Loop Closure Detectionmentioning

confidence: 99%

SVG-Loop: Semantic–Visual–Geometric Information-Based Loop Closure Detection

Yuan

Zhou

et al. 2021

Remote Sensing

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Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level image information and multi-information fusion can improve the robustness of place recognition, a semantic–visual–geometric information-based loop closure detection algorithm (SVG-Loop) is proposed in this paper. In detail, to reduce the interference of dynamic features, a semantic bag-of-words model was firstly constructed by connecting visual features with semantic labels. Secondly, in order to improve detection robustness in different scenes, a semantic landmark vector model was designed by encoding the geometric relationship of the semantic graph. Finally, semantic, visual, and geometric information was integrated by fuse calculation of the two modules. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference.

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Section: Semantic-information-based Loop Closure Detectionmentioning

confidence: 99%

SVG-Loop: Semantic–Visual–Geometric Information-Based Loop Closure Detection

Yuan

Zhou

et al. 2021

Remote Sensing

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“…The image contains a variety of information, so the most basic link to restore the real 3D scene from the image taken by UAV is image matching. At present, image matching is currently used extensively in target tracking [1][2][3][4], 3D reconstruction [5][6][7][8], visual SLAM [9][10][11], UAV obstacle avoidance navigation [12,13], and land surveying and mapping [14]. However, one of the common situations in real life is in a low-light environment photographed by UAV.…”

Section: Introductionmentioning

confidence: 99%

Fast Matching Method of UAV Aerial Photography Enhanced Low Illumination Image

Liu

et al. 2022

International Journal of Aerospace Engineering

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Aiming at the problems of insufficient image contrast in three-dimensional reconstruction of UAV in low illumination environment and the unstable iteration times of the RANSAC algorithm in the feature matching process, real-time matching method of UAV aerial image is proposed. First, a new image enhancement algorithm is applied to the image to enhance its quality and visibility. Second, the enhanced fast algorithm in ORB extracts the feature points from the preprocessed image, and cross-matching performs rough matching. Finally, the PROSAC algorithm solves the homography matrix by selecting the highest quality interior points from the extracted feature points. To improve the matching accuracy, some exterior points that do not conform to the geometric characteristics of the image are removed based on the homography matrix and the set mismatch threshold. The results show that the improved ORB algorithm is applied to the low illumination environment of UAV aerial photography, the image matching accuracy in 3D reconstruction is improved, and the correct matching rate tends to 97.24~99.39%. The relevant research findings and conclusions provide a fast and effective method for UAV image matching in different low illumination environments.

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