Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features

Gang, Xu; Li, Xiang; Zhang, Xingyu; Xing, Guangxin; Pan, Feng

doi:10.3390/app12010062

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…The system detects a feature point on a dynamic object using an enhanced lightweight YOLOv5-based method [22]. After that, position is estimated using the feature points that were kept, and a dense point cloud map is produced to satisfy navigation and path planning requirements [23]. The experimental findings demonstrate the good real-time performance and ability of the algorithm presented in this research to increase the system accuracy in dynamic circumstances [24].…”

Section: Slam (Simultaneous Localization and Mappingmentioning

confidence: 75%

Visual Simultaneous Localization and Mapping Optimization Method Based on Object Detection in Dynamic Scene

Zhu,

Cheng,

Zhuang

et al. 2024

Applied Sciences

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SLAM (Simultaneous Localization and Mapping), as one of the basic functions of mobile robots, has become a hot topic in the field of robotics this year. The majority of SLAM systems in use today, however, disregard the impact of dynamic objects on the system by defining the external environment as static. A SLAM system suitable for dynamic scenes is proposed, aiming at the issue that dynamic objects in real scenes can affect the localization accuracy and map effect of traditional visual SLAM systems. Initially, the enhanced lightweight YOLOv5s target detection algorithm is employed to detect dynamic objects in each frame of the image. Simultaneously, an assessment is conducted on the feature points present on dynamic objects to determine their potential impact on system accuracy, subsequently guiding the decision to retain or exclude these feature points. The preserved static feature points are then utilized for pose estimation and map construction. Experiments on the publicly available TUM dataset and the KITTI dataset are conducted to compare the system in this paper with ORB-SLAM 3, DS-SLAM, and DynaSLAM, and the algorithm is verified to have better performance.

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Section: Slam (Simultaneous Localization and Mappingmentioning

confidence: 75%

Visual Simultaneous Localization and Mapping Optimization Method Based on Object Detection in Dynamic Scene

Zhu,

Cheng,

Zhuang

et al. 2024

Applied Sciences

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“…Considering the above shortcomings, many researchers began to use the method of multimodal data fusion combined with adaptive Gaussian kernel to replace the above scheme. Several methods based on two-modal fusion are used [46][47][48] to demonstrate the advantages of crowd counting in terms of day and night illumination, occlusion, and scale transformation by obtaining fused features. Twostream models [49,50] are proposed to fuse hierarchical cross-modal features to achieve fully representative shared features.…”

Section: Related Workmentioning

confidence: 99%

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Ren

2022

Scientific Programming

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Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models.

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“…In Ref. [16], the authors combined deep learning and RGB-D sequences to take advantage of all the RGB-D information provided by Kinect. Their efforts included fussing the color and depth information with three techniques, namely, early, mid, and late fusion.…”

mentioning

confidence: 99%

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points

Lomas-Barrie,

Suarez-Espinoza,

Hernandez-Chavez

et al. 2023

Sensors

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Scene classification in autonomous navigation is a highly complex task due to variations, such as light conditions and dynamic objects, in the inspected scenes; it is also a challenge for small-factor computers to run modern and highly demanding algorithms. In this contribution, we introduce a novel method for classifying scenes in simultaneous localization and mapping (SLAM) using the boundary object function (BOF) descriptor on RGB-D points. Our method aims to reduce complexity with almost no performance cost. All the BOF-based descriptors from each object in a scene are combined to define the scene class. Instead of traditional image classification methods such as ORB or SIFT, we use the BOF descriptor to classify scenes. Through an RGB-D camera, we capture points and adjust them onto layers than are perpendicular to the camera plane. From each plane, we extract the boundaries of objects such as furniture, ceilings, walls, or doors. The extracted features compose a bag of visual words classified by a support vector machine. The proposed method achieves almost the same accuracy in scene classification as a SIFT-based algorithm and is 2.38× faster. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and robustness for the 7-Scenes and SUNRGBD datasets.

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Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features

Cited by 5 publications

References 34 publications

Visual Simultaneous Localization and Mapping Optimization Method Based on Object Detection in Dynamic Scene

Visual Simultaneous Localization and Mapping Optimization Method Based on Object Detection in Dynamic Scene

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points

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