Fast and Robust Loop-Closure Detection via Convolutional Auto-Encoder and Motion Consensus

Ma, Jiayi; Wang, Shenyue; Zhang, Kaining; He, Zheng; Huang, Jun; Mei, Xiaoguang

doi:10.1109/tii.2021.3120141

Cited by 14 publications

(2 citation statements)

References 38 publications

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“…The LCD problem is the process of determining the correlation between current and historical data to identify whether a location has been reached before. The essence of the LCD problem is to reduce the cumulative error in map construction [19]. With the development of computer vision, the LCD algorithm based on appearance information has become the mainstream algorithm in the early stage, among which BoVW is the most common algorithm [20].…”

Section: Loop Closure Detection (Lcd)mentioning

confidence: 99%

A Semantic Topology Graph to Detect Re-Localization and Loop Closure of the Visual Simultaneous Localization and Mapping System in a Dynamic Environment

Wang,

Zhang,

et al. 2023

Sensors

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Simultaneous localization and mapping (SLAM) plays a crucial role in the field of intelligent mobile robots. However, the traditional Visual SLAM (VSLAM) framework is based on strong assumptions about static environments, which are not applicable to dynamic real-world environments. The correctness of re-localization and recall of loop closure detection are both lower when the mobile robot loses frames in a dynamic environment. Thus, in this paper, the re-localization and loop closure detection method with a semantic topology graph based on ORB-SLAM2 is proposed. First, we use YOLOv5 for object detection and label the recognized dynamic and static objects. Secondly, the topology graph is constructed using the position information of static objects in space. Then, we propose a weight expression for the topology graph to calculate the similarity of topology in different keyframes. Finally, the re-localization and loop closure detection are determined based on the value of topology similarity. Experiments on public datasets show that the semantic topology graph is effective in improving the correct rate of re-localization and the accuracy of loop closure detection in a dynamic environment.

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Section: Loop Closure Detection (Lcd)mentioning

confidence: 99%

A Semantic Topology Graph to Detect Re-Localization and Loop Closure of the Visual Simultaneous Localization and Mapping System in a Dynamic Environment

Wang,

Zhang,

et al. 2023

Sensors

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show abstract

“…Building on prior work, Xia et al [16] used the deep learning network PCANet to extract features from images, demonstrating that features extracted by this network outperform manually designed features. MA et al [17] designed a lightweight autoencoder feature extractor and introduced a local matching algorithm based on motion-vector consistency constraints, ensuring high recall rates while enhancing real-time performance. Zhang et al [18] combined residual networks and capsule networks to simultaneously extract shallow geometric features and deep semantic features from images, reducing noise in images and accelerating model convergence.…”

Section: Introductionmentioning

confidence: 99%

Loop Closure Detection Based on Compressed ConvNet Features in Dynamic Environments

Jiang,

Zhou,

Sun

2023

Applied Sciences

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In dynamic environments, convolutional neural networks (CNNs) often produce image feature maps with significant redundancy due to external factors such as moving objects and occlusions. These feature maps are inadequate as precise image descriptors for similarity measurement, hindering loop closure detection. Addressing this issue, this paper proposes feature compression of convolutional neural network output. The approach is detailed as follows: (1) employing ResNet152 as the backbone feature-extraction network, a Siamese neural network is constructed to enhance the efficiency of feature extraction; (2) utilizing KL transformation to extract principal components from the backbone network’s output, thereby eliminating redundant information; (3) employing the compressed features as input for NetVLAD to construct a spatially informed feature descriptor for similarity measurement. Experimental results demonstrate that, on the New College dataset, the proposed improved method exhibits an approximately 9.98% enhancement in average accuracy compared to the original network. On the City Center dataset, there is an improvement of approximately 2.64%, with an overall increase of about 23.51% in time performance. These findings indicate that the enhanced ResNet152 performs better than the original network in environments with more moving objects and occlusions.

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Visual Loop Closure Detection Based on SqueezeNet Multi-layer Feature Fusion and Adaptive Range Matching Algorithm

2023

J Intell Robot Syst

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Fast and Robust Loop-Closure Detection via Convolutional Auto-Encoder and Motion Consensus

Cited by 14 publications

References 38 publications

A Semantic Topology Graph to Detect Re-Localization and Loop Closure of the Visual Simultaneous Localization and Mapping System in a Dynamic Environment

A Semantic Topology Graph to Detect Re-Localization and Loop Closure of the Visual Simultaneous Localization and Mapping System in a Dynamic Environment

Loop Closure Detection Based on Compressed ConvNet Features in Dynamic Environments

Visual Loop Closure Detection Based on SqueezeNet Multi-layer Feature Fusion and Adaptive Range Matching Algorithm

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