A Robust Feature Matching Strategy for Fast and Effective Visual Place Recognition in Challenging Environmental Conditions

Arshad, Saba; Kim, Gon–Woo

doi:10.1007/s12555-021-0927-x

Cited by 8 publications

(3 citation statements)

References 47 publications

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“…Recently, one such feature matching method has been presented in [20], where along with the appearance matching, spatial consistency is ensured for point features, ensuring the correct alignment of the match correspondences. Although the method successfully achieves improvement in place matching performance, it suffers from the non-detection of features in extreme seasonal variations and occlusion caused by the dynamic objects.…”

Section: Appearance-based Methodsmentioning

confidence: 99%

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SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions

Arshad,

Park

2024

Sensors

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Robust visual place recognition (VPR) enables mobile robots to identify previously visited locations. For this purpose, the extracted visual information and place matching method plays a significant role. In this paper, we critically review the existing VPR methods and group them into three major categories based on visual information used, i.e., handcrafted features, deep features, and semantics. Focusing the benefits of convolutional neural networks (CNNs) and semantics, and limitations of existing research, we propose a robust appearance-based place recognition method, termed SVS-VPR, which is implemented as a hierarchical model consisting of two major components: global scene-based and local feature-based matching. The global scene semantics are extracted and compared with pre-visited images to filter the match candidates while reducing the search space and computational cost. The local feature-based matching involves the extraction of robust local features from CNN possessing invariant properties against environmental conditions and a place matching method utilizing semantic, visual, and spatial information. SVS-VPR is evaluated on publicly available benchmark datasets using true positive detection rate, recall at 100% precision, and area under the curve. Experimental findings demonstrate that SVS-VPR surpasses several state-of-the-art deep learning-based methods, boosting robustness against significant changes in viewpoint and appearance while maintaining efficient matching time performance.

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Section: Appearance-based Methodsmentioning

confidence: 99%

“…Conventional image matching approaches that rely on handcrafted local features [44] initially identify distinctive keypoints within images and then apply robust descriptors for image representation [20]. Those descriptors are then matched through techniques such as RANSAC.…”

Section: Local Feature-based Appearance Matchingmentioning

confidence: 99%

SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions

Arshad,

Park

2024

Sensors

Self Cite

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“…To model the saliency of local features from different dimensions, the approach incorporates three attention modules that consider individual, spatial, and cluster dimensions [21]. In the study [22], robust feature selection and matching processes are investigated to enhance the accuracy of place recognition. They integrate a BoW vocabulary with a feature matcher to adapt to varying environmental conditions.…”

Section: Feature Descriptionmentioning

confidence: 99%

Contextual Patch-NetVLAD: Context-Aware Patch Feature Descriptor and Patch Matching Mechanism for Visual Place Recognition

Sun,

Chen,

Huang

et al. 2024

Sensors

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The goal of visual place recognition (VPR) is to determine the location of a query image by identifying its place in a collection of image databases. Visual sensor technologies are crucial for visual place recognition as they allow for precise identification and location of query images within a database. Global descriptor-based VPR methods face the challenge of accurately capturing the local specific regions within a scene; consequently, it leads to an increasing probability of confusion during localization in such scenarios. To tackle feature extraction and feature matching challenges in VPR, we propose a modified patch-NetVLAD strategy that includes two new modules: a context-aware patch descriptor and a context-aware patch matching mechanism. Firstly, we propose a context-driven patch feature descriptor to overcome the limitations of global and local descriptors in visual place recognition. This descriptor aggregates features from each patch’s surrounding neighborhood. Secondly, we introduce a context-driven feature matching mechanism that utilizes cluster and saliency context-driven weighting rules to assign higher weights to patches that are less similar to densely populated or locally similar regions for improved localization performance. We further incorporate both of these modules into the patch-NetVLAD framework, resulting in a new approach called contextual patch-NetVLAD. Experimental results are provided to show that our proposed approach outperforms other state-of-the-art methods to achieve a Recall@10 score of 99.82 on Pittsburgh30k, 99.82 on FMDataset, and 97.68 on our benchmark dataset.

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A Comparative Field Study of Global Pose Estimation Algorithms in Subterranean Environments

Stathoulopoulos,

Koval,

Nikolakopoulos

2024

Int. J. Control Autom. Syst.

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A Robust Feature Matching Strategy for Fast and Effective Visual Place Recognition in Challenging Environmental Conditions

Cited by 8 publications

References 47 publications

SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions

SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions

Contextual Patch-NetVLAD: Context-Aware Patch Feature Descriptor and Patch Matching Mechanism for Visual Place Recognition

A Comparative Field Study of Global Pose Estimation Algorithms in Subterranean Environments

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