Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot

Zhang, Xin; Zheng, Li; Tan, Zhenhua; Li, Suo

doi:10.3390/s22197137

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…The application of SLAM technology is widespread in industries, including mobile robots, virtual reality [3,4], smart mobile homes, and autonomous driving [5]. Visual sensors are accessible and can capture detailed images; thus, visual SLAM with cameras has broad appeal [6]. However, variations with respect to perspective, lighting, weather, and interference from moving objects may all have a detrimental effect on the precision of the entire system when visual SLAM mobile robots perform autonomous positioning and navigation [7].…”

Section: Introductionmentioning

confidence: 99%

Loop Closure Detection Method Based on Similarity Differences between Image Blocks

Huang,

Zhang

et al. 2023

Sensors

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Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map’s general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate’s accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality.

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Section: Introductionmentioning

confidence: 99%

Loop Closure Detection Method Based on Similarity Differences between Image Blocks

Huang,

Zhang

et al. 2023

Sensors

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“…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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Deep learning-based recognition of stained tongue coating images

Zhong,

Xin,

Peng

et al. 2024

Digital Chinese Medicine

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Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot

Cited by 3 publications

References 40 publications

Loop Closure Detection Method Based on Similarity Differences between Image Blocks

Loop Closure Detection Method Based on Similarity Differences between Image Blocks

Loop Closure Detection Based on Compressed ConvNet Features in Dynamic Environments

Deep learning-based recognition of stained tongue coating images

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