Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning

Liang Chen; Sheng Jin; Zhoujun Xia

doi:10.3390/s21010310

Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning

Sensors ◽

10.3390/s21010310 ◽

2021 ◽

Vol 21 (1) ◽

pp. 310

Author(s):

Liang Chen ◽

Sheng Jin ◽

Zhoujun Xia

Keyword(s):

Distance Learning ◽

Large Scale ◽

Critical Role ◽

Distance Constraint ◽

Place Recognition ◽

Learning Method ◽

Localization And Mapping ◽

Depth Analysis ◽

Visual Place Recognition ◽

Distance Relationship

The application of deep learning is blooming in the field of visual place recognition, which plays a critical role in visual Simultaneous Localization and Mapping (vSLAM) applications. The use of convolutional neural networks (CNNs) achieve better performance than handcrafted feature descriptors. However, visual place recognition is still a challenging task due to two major problems, i.e., perceptual aliasing and perceptual variability. Therefore, designing a customized distance learning method to express the intrinsic distance constraints in the large-scale vSLAM scenarios is of great importance. Traditional deep distance learning methods usually use the triplet loss which requires the mining of anchor images. This may, however, result in very tedious inefficient training and anomalous distance relationships. In this paper, a novel deep distance learning framework for visual place recognition is proposed. Through in-depth analysis of the multiple constraints of the distance relationship in the visual place recognition problem, the multi-constraint loss function is proposed to optimize the distance constraint relationships in the Euclidean space. The new framework can support any kind of CNN such as AlexNet, VGGNet and other user-defined networks to extract more distinguishing features. We have compared the results with the traditional deep distance learning method, and the results show that the proposed method can improve the performance by 19–28%. Additionally, compared to some contemporary visual place recognition techniques, the proposed method can improve the performance by 40%/36% and 27%/24% in average on VGGNet/AlexNet using the New College and the TUM datasets, respectively. It’s verified the method is capable to handle appearance changes in complex environments.

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Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018034 ◽

2019 ◽

Vol 33 ◽

pp. 8034-8041 ◽

Cited By ~ 1

Author(s):

Kai Liu ◽

Hua Wang ◽

Fei Han ◽

Hao Zhang

Keyword(s):

Large Scale ◽

Optimization Problem ◽

Data Sets ◽

Place Recognition ◽

Benchmark Data ◽

Localization And Mapping ◽

Novel Method ◽

Visual Place Recognition ◽

Holistic Representation

Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications.

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

2018 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2018.8500686 ◽

2018 ◽

Cited By ~ 2

Author(s):

Jianliang Zhu ◽

Yunfeng Ai ◽

Bin Tian ◽

Dongpu Cao ◽

Sebastian Scherer

Keyword(s):

Large Scale ◽

Place Recognition ◽

Visual Place Recognition

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Variational Bayesian Approach to Condition-Invariant Feature Extraction for Visual Place Recognition

Applied Sciences ◽

10.3390/app11198976 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8976

Author(s):

Junghyun Oh ◽

Gyuho Eoh

Keyword(s):

Large Scale ◽

Environmental Changes ◽

Place Recognition ◽

Stochastic Variational Inference ◽

Invariant Features ◽

Variational Autoencoder ◽

Model Condition ◽

Visual Place Recognition ◽

Perceptual Changes

As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract condition-invariant features for visual place recognition in a changing environment. Under the assumption that a latent representation of the variational autoencoder can be divided into condition-invariant and condition-sensitive features, a new structure of the variation autoencoder is proposed and a variational lower bound is derived to train the model. After training the model, condition-invariant features are extracted from test images to calculate the similarity matrix, and the places can be recognized even in severe environmental changes. Experiments were conducted to verify the proposed method, and the experimental results showed that our assumption was reasonable and effective in recognizing places in changing environments.

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Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-312 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 313-1-313-7

Author(s):

Raffaele Imbriaco ◽

Egor Bondarev ◽

Peter H.N. de With

Keyword(s):

Image Matching ◽

Large Scale ◽

Place Recognition ◽

Aggregation Method ◽

Local Descriptors ◽

Multi Scale ◽

Vector Construction ◽

Visual Place Recognition ◽

Different Sources ◽

Geometric Verification

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.

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A Multi-Domain Feature Learning Method for Visual Place Recognition

2019 International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2019.8793752 ◽

2019 ◽

Cited By ~ 4

Author(s):

Peng Yin ◽

Lingyun Xu ◽

Xueqian Li ◽

Chen Yin ◽

Yingli Li ◽

...

Keyword(s):

Feature Learning ◽

Place Recognition ◽

Learning Method ◽

Visual Place Recognition

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LANDMARK-BASED VISUAL PLACE RECOGNITION

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i11.035 ◽

2021 ◽

Vol 5 (11) ◽

Author(s):

Swapnali Gavali ◽

Dr. Bashirahamad Momin

Keyword(s):

Object Recognition ◽

Large Scale ◽

Nearest Neighbor ◽

Fine Tuning ◽

Place Recognition ◽

K Nearest Neighbor ◽

Data Set ◽

Similar Images ◽

Triplet Loss ◽

Visual Place Recognition

— Landmark recognition is one type of problem of object recognition that has not been well solved. The classical techniques used for object recognition can not be applied directly due to a large number of landmarks and a highly imbalanced dataset. This paper presents the application of a triplet network for large scale landmarkbased visual place recognition. By fine-tuning pre-trained convolutional neural network (CNN) and minimizing triplet loss, the triplet network can learn appropriate metrics so that most similar images can be retrieved through algorithms for the k-nearest neighbor (KNN). The performance of the proposed method is evaluated on a data set for the recognition of real-world landmarks.

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Nursing professionals’ attitudes toward use of physical restraints in Styrian nursing homes Austria

Pflege ◽

10.1024/1012-5302/a000649 ◽

2019 ◽

Vol 32 (1) ◽

pp. 57-63

Author(s):

Hannes Mayerl ◽

Tanja Trummer ◽

Erwin Stolz ◽

Éva Rásky ◽

Wolfgang Freidl

Keyword(s):

Nursing Homes ◽

Large Scale ◽

Critical Role ◽

Physical Restraint ◽

Care Practice ◽

Physical Restraints ◽

Convenience Sample ◽

Restraint Use ◽

Positive Attitudes ◽

Nursing Professionals

Abstract. Background: Given that nursing staff play a critical role in the decision regarding use of physical restraints, research has examined nursing professionals’ attitudes toward this practice. Aim: Since nursing professionals’ views on physical restraint use have not yet been examined in Austria to date, we aimed to explore nursing professionals’ attitudes concerning use of physical restraints in nursing homes of Styria (Austria). Method: Data were collected from a convenience sample of nursing professionals (N = 355) within 19 Styrian nursing homes, based on a cross-sectional study design. Attitudes toward the practice of restraint use were assessed by means of the Maastricht Attitude Questionnaire in the German version. Results: The overall results showed rather positive attitudes toward the use of physical restraints, yet the findings regarding the sub-dimensions of the questionnaire were mixed. Although nursing professionals tended to deny “good reasons” for using physical restraints, they evaluated the consequences of physical restraint use rather positive and considered restraint use as an appropriate health care practice. Nursing professionals’ views regarding the consequences of using specific physical restraints further showed that belts were considered as the most restricting and discomforting devices. Conclusions: Overall, Austrian nursing professionals seemed to hold more positive attitudes toward the use of physical restraints than counterparts in other Western European countries. Future nationwide large-scale surveys will be needed to confirm our findings.

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