A Survey on Deep Visual Place Recognition

Masone, Carlo; Caputo, Barbara

doi:10.1109/access.2021.3054937

Cited by 105 publications

(54 citation statements)

References 231 publications

(508 reference statements)

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“…There are several works in the literature that tackle the problem of location estimation. These can be roughly classified into two categories according to [6,28,29]: (i) approaches restricted to specific environments or imagery, and (ii) planet-scale approaches without any restrictions. Our approach belongs to the second category.…”

Section: Related Workmentioning

confidence: 99%

Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

Kordopatis-Zilos

Galopoulos

Papadopoulos

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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In this paper, we address the problem of global-scale image geolocation, proposing a mixed classification-retrieval scheme. Unlike other methods that strictly tackle the problem as a classification or retrieval task, we combine the two practices in a unified solution leveraging the advantages of each approach with two different modules. The first leverages the EfficientNet architecture to assign images to a specific geographic cell in a robust way. The second introduces a new residual architecture that is trained with contrastive learning to map input images to an embedding space that minimizes the pairwise geodesic distance of same-location images. For the final location estimation, the two modules are combined with a search-within-cell scheme, where the locations of most similar images from the predicted geographic cell are aggregated based on a spatial clustering scheme. Our approach demonstrates very competitive performance on four public datasets, achieving new state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km range on Im2GPS3k. CCS CONCEPTS• Computing methodologies → Computer vision problems;• Information systems → Geographic information systems.

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Section: Related Workmentioning

confidence: 99%

Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

Kordopatis-Zilos

Galopoulos

Papadopoulos

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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“…Loop closure detection is an important task in the field of monocular SLAM, and it has also been a hot research topic in recent years. References [30][31][32] give a variety of loop detection and location recognition algorithms. These methods have their advantages in efficiency, accuracy, and generalization.…”

Section: Related Workmentioning

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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“…Thus, CNN-based VPR systems have become a research hotspot. To address environmental changes better, landmark-based VPR frameworks combined with CNN features gradually become the mainstream [15]. These frameworks considered that VPR should only reserve salient landmark regions and it was not necessary to keep the whole CNN features of images.…”

Section: Related Workmentioning

confidence: 99%

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

2021

KSII TIIS

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Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

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A Survey on Deep Visual Place Recognition

Cited by 105 publications

References 231 publications

Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

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

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

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