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

Yuan, Zhian; Xu, Ke; Zhou, Xiaoyu; Deng, Bin; Ma, Yanxin

doi:10.3390/rs13173520

Cited by 19 publications

(10 citation statements)

References 52 publications

(74 reference statements)

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“…Recently, several approaches have been developed that leverage semantic graph models to represent scenes using semantic segmentation outcomes, leading to more robust visual place recognition [40][41][42][43]. Authors of [40] use the Local Semantic Tensor in conjunction with semantic edge features extracted from semantic segmentation masks to detect correct place matches, while [41][42][43] generates topological connectivity graphs using pixelwise semantic labels in the scene to match the spatial information.…”

Section: Semantics-based Methodsmentioning

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

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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: Semantics-based Methodsmentioning

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

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“…Zhang et al [33] used semantic information to estimate rigid objects in the scene. Yuan et al [34] constructed a word bag model using semantic tags to reduce the impact of dynamic objects on the SLAM system. However, in the case of actual industrial implementation, visual SLAM needs to satisfy both accuracy and real-time, so there is still a lot of research space for it.…”

Section: Relate Workmentioning

confidence: 99%

Real-Time Dynamic SLAM Algorithm Based on Deep Learning

Luo

Huang

2022

IEEE Access

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In the traditional visual simultaneous localization and mapping (SLAM), the strong static assumption leads to a large degradation in the accuracy of visual SLAM in dynamic environments. For this reason, many scholars incorporate semantic segmentation networks into the visual SLAM framework to extract dynamic information in images. However, most semantic segmentation networks consume a lot of computing time due to the large model size, which leads to the algorithm's inability to meet real-time requirements in practical applications. In this paper, a real-time visual SLAM algorithm based on deep learning is proposed. This novel algorithm is based on ORB-SLAM2, and a parallel semantic thread based on the lightweight object detection network YOLOv5s is designed, which enables us to get semantic information in the scene more quickly. In the tracking thread, an optimized homography matrix module is proposed, which utilizes semantic information to optimize and solve the homography matrix so that we can calculate a more accurate optical flow vector. In the optical flow module, the semantic information is used to narrow down the calculation range of the optical flow value to improve the real-time performance of the system, and the dynamic feature points in the image are removed by the optical flow mask to improve the accuracy of the system. Experimental results show that compared with ORB-SLAM2, DynaSLAM, and other excellent visual SLAM algorithms, this algorithm can effectively reduce the absolute trajectory error of visual SLAM in dynamic environments. Compared with other deep learning-based visual SLAM algorithms, the real-time performance of this algorithm is also significantly improved.INDEX TERMS SLAM, dynamic environment, semantic, optical flow method, pose estimation.

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“…The critical problem of dynamic SLAM is to segment dynamic objects in the environment and determine the correct data association. We classify dynamic SLAM solutions into two classes: geometry-based methods [10][11][12][13][14][15][16][17][18][19] and semantic-based methods [20][21][22][23][24][25][26][27][28][29][30][31]. Geometry-based approaches detect dynamic objects in a scene by constructing multiple sets of geometric constraints.…”

Section: Introductionmentioning

confidence: 99%

DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information

Long

et al. 2022

Remote Sensing

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Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application in real dynamic scenarios. To improve the robustness and efficiency of the system in dynamic environments, this paper proposes a dynamic RGBD SLAM based on a combination of geometric and semantic information (DGS-SLAM). First, a dynamic object detection module based on the multinomial residual model is proposed, which executes the motion segmentation of the scene by combining the motion residual information of adjacent frames and the potential motion information of the semantic segmentation module. Second, a camera pose tracking strategy using feature point classification results is designed to achieve robust system tracking. Finally, according to the results of dynamic segmentation and camera tracking, a semantic segmentation module based on a semantic frame selection strategy is designed for extracting potential moving targets in the scene. Extensive evaluation in public TUM and Bonn datasets demonstrates that DGS-SLAM has higher robustness and speed than state-of-the-art dynamic RGB-D SLAM systems in dynamic scenes.

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

Cited by 19 publications

References 52 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

Real-Time Dynamic SLAM Algorithm Based on Deep Learning

DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information

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