Improved Loop Closure Detection Algorithm for VSLAM with Spatial Coordinate Index

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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A Loop Closure Detection Algorithm in Dynamic Scene

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/ccnt2018/24714 ◽

2018 ◽

Author(s):

Hui XU ◽

He-xin ZHANG ◽

Er-liang YAO ◽

Hai-tao SONG

Keyword(s):

Detection Algorithm ◽

Dynamic Scene ◽

Loop Closure ◽

Loop Closure Detection

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The loop closure Detection Algorithm Based on Bag of Semantic Word For Robot Navigation

10.1109/iciba50161.2020.9277317 ◽

2020 ◽

Author(s):

Pengcheng Yu ◽

Xiaogang Ruan ◽

Xiaoqing Zhu

Keyword(s):

Robot Navigation ◽

Detection Algorithm ◽

Loop Closure ◽

Loop Closure Detection

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Original Loop-Closure Detection Algorithm for Monocular vSLAM

Lecture Notes in Computer Science - Analysis of Images, Social Networks and Texts ◽

10.1007/978-3-319-73013-4_19 ◽

2017 ◽

pp. 210-220

Author(s):

Andrey Bokovoy ◽

Konstantin Yakovlev

Keyword(s):

Detection Algorithm ◽

Loop Closure ◽

Loop Closure Detection

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A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM

Journal of Electrical and Computer Engineering ◽

10.1155/2021/9978022 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Qiubo Zhong ◽

Xiaoyi Fang

Keyword(s):

Unsupervised Learning ◽

Recall Rate ◽

Detection Algorithm ◽

Detection Methods ◽

Bag Of Words ◽

Learning Method ◽

Visual Slam ◽

Loop Closure ◽

Loop Closure Detection ◽

Main Component

Loop closure detection serves as the fulcrum of improving the accuracy and precision in simultaneous localization and mapping (SLAM). The majority of loop detection methods extract artificial features, which fall short of learning comprehensive data information, but unsupervised learning as a typical deep learning method excels in self-access learning and clustering to analyze the similarity without handling the data. Moreover, the unsupervised learning method does solve restrictions on image quality and singleness semantics in many traditional SLAM methods. Therefore, a loop closure detection strategy based on an unsupervised learning method is proposed in this paper. The main component adopts BigBiGAN to extract features and establish an original bag of words. Then, the complete bag of words is used to detect loop closing. Finally, a considerable validation check of the ORB descriptor is added to verify the result and output outcome of loop closure detection. The proposed algorithm and other compared algorithms are, respectively, applied on Autolabor Pro1 to execute the indoor visual SLAM. The experiment shows that the proposed algorithm increases the recall rate by 20% compared with ORB-SLAM2 and LSD-SLAM. And it also improves at least 40.0% accuracy than others and reduces 14% time loss of ORB-SLAM2. Therefore, the presented SLAM based on BigBiGAN does benefit much the visual SLAM in the indoor environment.

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Loop Closure Detection Based on Multi-Scale Deep Feature Fusion

Applied Sciences ◽

10.3390/app9061120 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1120 ◽

Cited By ~ 3

Author(s):

Baifan Chen ◽

Dian Yuan ◽

Chunfa Liu ◽

Qian Wu

Keyword(s):

Feature Fusion ◽

Receptive Fields ◽

Detection Algorithm ◽

Image Features ◽

Mobile Robot Navigation ◽

Loop Closure ◽

Loop Closure Detection ◽

Multi Scale ◽

Deep Feature ◽

Illumination Changes

Loop closure detection plays a very important role in the mobile robot navigation field. It is useful in achieving accurate navigation in complex environments and reducing the cumulative error of the robot’s pose estimation. The current mainstream methods are based on the visual bag of word model, but traditional image features are sensitive to illumination changes. This paper proposes a loop closure detection algorithm based on multi-scale deep feature fusion, which uses a Convolutional Neural Network (CNN) to extract more advanced and more abstract features. In order to deal with the different sizes of input images and enrich receptive fields of the feature extractor, this paper uses the spatial pyramid pooling (SPP) of multi-scale to fuse the features. In addition, considering the different contributions of each feature to loop closure detection, the paper defines the distinguishability weight of features and uses it in similarity measurement. It reduces the probability of false positives in loop closure detection. The experimental results show that the loop closure detection algorithm based on multi-scale deep feature fusion has higher precision and recall rates and is more robust to illumination changes than the mainstream methods.

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