Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046546
Yonggong Han, Wen-Chung Liao, Jianxin Wang
Macrofungi refer to fungi with large fruiting bodies. In terms of systematic classification, the species of macrofungi come from the Discomycetes of Basidiomycota and Ascomycota. As the decomposer of nature, macrofungi play a key role in the carbon cycle of the earth and have strong research significance. However, there are many kinds of macrofungi, with a population of more than 10 000. It requires profound professional knowledge to identify them, which is a waste of manpower and material resources. In this study, we creatively proposed a new method based on convolutional neural network (CNN) to recognize macrofungi images. By combining the attention mechanism with the lightweight backbone model densely connected convolutional network (DenseNet), A-DenseNet model is proposed to complete the efficient classification task of macrofungi. The recognition accuracy of our model on the public macrofungi dataset reached 84.3%, and the recognition accuracy on the local macrofungi dataset reached 82.2%, which illustrates the excellent performance of our network in the macrofungi recognition task. This method is an effective supplement and reference for macrofungi classification task.
{"title":"Recognition of Macrofungi by Convolutional Neural Networks with Attention Mechanism","authors":"Yonggong Han, Wen-Chung Liao, Jianxin Wang","doi":"10.1109/ICARCE55724.2022.10046546","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046546","url":null,"abstract":"Macrofungi refer to fungi with large fruiting bodies. In terms of systematic classification, the species of macrofungi come from the Discomycetes of Basidiomycota and Ascomycota. As the decomposer of nature, macrofungi play a key role in the carbon cycle of the earth and have strong research significance. However, there are many kinds of macrofungi, with a population of more than 10 000. It requires profound professional knowledge to identify them, which is a waste of manpower and material resources. In this study, we creatively proposed a new method based on convolutional neural network (CNN) to recognize macrofungi images. By combining the attention mechanism with the lightweight backbone model densely connected convolutional network (DenseNet), A-DenseNet model is proposed to complete the efficient classification task of macrofungi. The recognition accuracy of our model on the public macrofungi dataset reached 84.3%, and the recognition accuracy on the local macrofungi dataset reached 82.2%, which illustrates the excellent performance of our network in the macrofungi recognition task. This method is an effective supplement and reference for macrofungi classification task.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046563
Mengxia He
In response to the backwardness of traditional agricultural irrigation technology in China, many places still use traditional manual watering, which is a serious waste of water resources and low irrigation efficiency, an intelligent agricultural water-saving irrigation control system based on the Internet of Things is designed, taking into account the characteristics of the moisture content of agricultural soil in different periods. This system uses wireless communication technology to transmit the soil moisture content, dry and wet conditions and other information inside the field obtained by the soil moisture detector to the control system in real time. On this basis, the control information is sent to each irrigation area using the wireless network to control the automatic valve opening and closing of the irrigation system, thus realizing automatic water supply and water-saving irrigation.
{"title":"Research on Rural Water-saving Intelligent Irrigation System Based on Internet of Things","authors":"Mengxia He","doi":"10.1109/ICARCE55724.2022.10046563","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046563","url":null,"abstract":"In response to the backwardness of traditional agricultural irrigation technology in China, many places still use traditional manual watering, which is a serious waste of water resources and low irrigation efficiency, an intelligent agricultural water-saving irrigation control system based on the Internet of Things is designed, taking into account the characteristics of the moisture content of agricultural soil in different periods. This system uses wireless communication technology to transmit the soil moisture content, dry and wet conditions and other information inside the field obtained by the soil moisture detector to the control system in real time. On this basis, the control information is sent to each irrigation area using the wireless network to control the automatic valve opening and closing of the irrigation system, thus realizing automatic water supply and water-saving irrigation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116122410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046578
Yongliang Zhang, Lingcong Nie, Ting Yu, F. Lu, Jin-Quan Huang
In this paper, a mathematical model of tandem turbine-based combined cycle (TBCC) engine is studied based on the component-level concept, and then the mode transition is focused on with the controller design. The aerodynamic thermodynamic equations are drawn out in the establishment of engine component-level model, and Newton-Raphson method is applied to solve the common operation equations. In addition, the modal transition process is simulated and analyzed, the mode transition operating point of the TBCC engine is determined in the flight trajectory. Thus, the combined engine modal transition control quantity adjustment plan is formulated. Finally, a multi-variable controller based on neural network estimation and inverse control is designed and verified in the TBCC simulation.
{"title":"Design Method of Mode Transition Control Law for TBCC Engine","authors":"Yongliang Zhang, Lingcong Nie, Ting Yu, F. Lu, Jin-Quan Huang","doi":"10.1109/ICARCE55724.2022.10046578","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046578","url":null,"abstract":"In this paper, a mathematical model of tandem turbine-based combined cycle (TBCC) engine is studied based on the component-level concept, and then the mode transition is focused on with the controller design. The aerodynamic thermodynamic equations are drawn out in the establishment of engine component-level model, and Newton-Raphson method is applied to solve the common operation equations. In addition, the modal transition process is simulated and analyzed, the mode transition operating point of the TBCC engine is determined in the flight trajectory. Thus, the combined engine modal transition control quantity adjustment plan is formulated. Finally, a multi-variable controller based on neural network estimation and inverse control is designed and verified in the TBCC simulation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"125 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121189328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046642
Hao Chen, Yang Bai, Miaomiao Wei
Taking the construction of knowledge atlas as the main line, this paper introduces the concept definition, architecture and construction technology of knowledge atlas, discusses the problems and challenges in the iterative process of constructing knowledge atlas, and finally looks forward to the possible research directions in the future. In order to help interested readers fully understand and understand the technology.
{"title":"Research and Development Trend of Knowledge Graph Construction Technology","authors":"Hao Chen, Yang Bai, Miaomiao Wei","doi":"10.1109/ICARCE55724.2022.10046642","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046642","url":null,"abstract":"Taking the construction of knowledge atlas as the main line, this paper introduces the concept definition, architecture and construction technology of knowledge atlas, discusses the problems and challenges in the iterative process of constructing knowledge atlas, and finally looks forward to the possible research directions in the future. In order to help interested readers fully understand and understand the technology.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126125544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046555
Huaying Liu, Lei Xue
In order to realize the complex operation skills learning of a UR 10 collaborative robot, we propose a dynamic-motion-primitive robot skill learning algorithm based on reinforcement learning and imitation learning. Shapes of demonstrated trajectories is re-trained with dynamic motion primitives, and the robot arm replaces the explicit coordinates to reach the target point in an exploratory manner. Experiment results show that the optimized trajectory of the robot can preserves the shape of the teach-and-fit track well.
{"title":"Robot Skill Learning Algorithm Based on Dynamic Motion Primitive","authors":"Huaying Liu, Lei Xue","doi":"10.1109/ICARCE55724.2022.10046555","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046555","url":null,"abstract":"In order to realize the complex operation skills learning of a UR 10 collaborative robot, we propose a dynamic-motion-primitive robot skill learning algorithm based on reinforcement learning and imitation learning. Shapes of demonstrated trajectories is re-trained with dynamic motion primitives, and the robot arm replaces the explicit coordinates to reach the target point in an exploratory manner. Experiment results show that the optimized trajectory of the robot can preserves the shape of the teach-and-fit track well.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131520907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046553
Zhisheng Lie, S. Ren, Qiong Liu
Different objects with similar spectral features are common in remote sensing images, such as trees and low-vegetation, building and roads. It is important to segment them well for urban planning, traffic navigation, and so on. However, the existing multi-level feature fusion methods ignore the relationship among all features of each level, making these objects hard to distinguish. In this paper, we propose a Dualpath Multi-level Feature Fusion Network (DMFFN) to make good use of the features of backbone. This network includes two paths to fuse the features and model the dependences between them. After getting the features from two paths, we utilize a cross-attention module to decoder them for better segmentation. Experimental results over two datasets show that DMFFN outperforms state-of-the-art methods.
{"title":"Dual-Path Multi-Level Feature Fusion Network for Semantic Segmentation of Remote Sensing Image","authors":"Zhisheng Lie, S. Ren, Qiong Liu","doi":"10.1109/ICARCE55724.2022.10046553","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046553","url":null,"abstract":"Different objects with similar spectral features are common in remote sensing images, such as trees and low-vegetation, building and roads. It is important to segment them well for urban planning, traffic navigation, and so on. However, the existing multi-level feature fusion methods ignore the relationship among all features of each level, making these objects hard to distinguish. In this paper, we propose a Dualpath Multi-level Feature Fusion Network (DMFFN) to make good use of the features of backbone. This network includes two paths to fuse the features and model the dependences between them. After getting the features from two paths, we utilize a cross-attention module to decoder them for better segmentation. Experimental results over two datasets show that DMFFN outperforms state-of-the-art methods.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046442
Weiwei Miao, Zeng Zeng, Changzhi Teng, Rui Zhang
Lightweight edge systems are complicated due to their limited execution capabilities and communication problems. Due to the highly complex and dynamic nature of real-time tasks, scheduling services to which nodes faces the unique challenges of resource allocation and network bandwidth coordination. To address these challenges, in this paper we introduce a scheduling mechanism called RASM. Using this mechanism, intelligent real-time tasks at the lightweight edge can be efficiently assigned to the appropriate computing nodes without waiting in long queues at the edge nodes or being allocated to the cloud indiscriminately. Our approach is a novel technology for scheduling resources in the server environment. Particularly, our method defines a cache list to record the task execution location, priority and system remaining resources, so as to unload the task to the appropriate node for execution and maximize the quality of service. Our approach is scalable and efficient with the provided infrastructure resources. Our evaluation shows that over the same conditions, RASM reduces the delay by more than 15% compared with the Cloud Only environment, and solves more tasks than the Edge Only environment.
{"title":"Resource-Aware Scheduling Mechanism for Real-Time Tasks in Lightweight Edge Systems","authors":"Weiwei Miao, Zeng Zeng, Changzhi Teng, Rui Zhang","doi":"10.1109/ICARCE55724.2022.10046442","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046442","url":null,"abstract":"Lightweight edge systems are complicated due to their limited execution capabilities and communication problems. Due to the highly complex and dynamic nature of real-time tasks, scheduling services to which nodes faces the unique challenges of resource allocation and network bandwidth coordination. To address these challenges, in this paper we introduce a scheduling mechanism called RASM. Using this mechanism, intelligent real-time tasks at the lightweight edge can be efficiently assigned to the appropriate computing nodes without waiting in long queues at the edge nodes or being allocated to the cloud indiscriminately. Our approach is a novel technology for scheduling resources in the server environment. Particularly, our method defines a cache list to record the task execution location, priority and system remaining resources, so as to unload the task to the appropriate node for execution and maximize the quality of service. Our approach is scalable and efficient with the provided infrastructure resources. Our evaluation shows that over the same conditions, RASM reduces the delay by more than 15% compared with the Cloud Only environment, and solves more tasks than the Edge Only environment.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132260110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046637
Weida Ni, Y. Lou, Xiaolu Xu, Z. Shan, Shouzhong Xue, Wei Wang
Seismic random noise reduction is a critical task for seismic data processing. However, because seismic data is a representatively broadband signal, it is challenging to distinguish and attenuate random noise that present throughout the whole frequency range. Furthermore, it might be challenging to adjust the settings of denoising methods. To overcome the aforementioned problems, we suggest a multichannel filtering approach, i.e., noise-assisted multi-variate empirical mode decomposition (NA-MEMD) based multichannel singular spectrum analysis (MSSA). To filter noisy seismic data, we use NA-MEMD to decompose the noisy seismic data into a number of band-limited intrinsic mode functions (IMFs) with various center frequencies and bandwidths. Note that multiple channels of a particular IMF have the same dominant frequency aids in more reduction of random noise. Then, for separating random noise and maintaining valid signals, we apply MSSA to each IMF. The denoising outcome is then achieved by adding all the filtered IMFs. To demonstrate the validity and effectiveness of the suggested approach, we apply it to synthetic and 3D real data, and compare with traditional denoising techniques.
{"title":"Seismic Random Noise Attenuation via Noise Assisted-multivariate EMD Based MSSA","authors":"Weida Ni, Y. Lou, Xiaolu Xu, Z. Shan, Shouzhong Xue, Wei Wang","doi":"10.1109/ICARCE55724.2022.10046637","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046637","url":null,"abstract":"Seismic random noise reduction is a critical task for seismic data processing. However, because seismic data is a representatively broadband signal, it is challenging to distinguish and attenuate random noise that present throughout the whole frequency range. Furthermore, it might be challenging to adjust the settings of denoising methods. To overcome the aforementioned problems, we suggest a multichannel filtering approach, i.e., noise-assisted multi-variate empirical mode decomposition (NA-MEMD) based multichannel singular spectrum analysis (MSSA). To filter noisy seismic data, we use NA-MEMD to decompose the noisy seismic data into a number of band-limited intrinsic mode functions (IMFs) with various center frequencies and bandwidths. Note that multiple channels of a particular IMF have the same dominant frequency aids in more reduction of random noise. Then, for separating random noise and maintaining valid signals, we apply MSSA to each IMF. The denoising outcome is then achieved by adding all the filtered IMFs. To demonstrate the validity and effectiveness of the suggested approach, we apply it to synthetic and 3D real data, and compare with traditional denoising techniques.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124416144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046598
Chenjun Gao, Ganghui Bian, Yanzhi Dong, Xiaohu Yuan, Huaping Liu
When sufficient prior knowledge is lacking or manual annotation is difficult, solving the problem directly based on training samples of unknown category can greatly reduce the time cost. Therefore, we add unsupervised learning to the preliminary groundwork of image captioning for efficient image domain conversion to achieve batch generation of the required images. At the same time, more and more infrared images are being applied to assist decision making and environment perception. Generating more diverse and discriminative image captions in similar scenes will be effective in enhancing decision making and perception capabilities. Our infrared image caption model trained with reinforcement learning has satisfactory results both in terms of quantitative scores and in real scene tests.
{"title":"Infrared Image Captioning Based on Unsupervised Learning and Reinforcement Learning","authors":"Chenjun Gao, Ganghui Bian, Yanzhi Dong, Xiaohu Yuan, Huaping Liu","doi":"10.1109/ICARCE55724.2022.10046598","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046598","url":null,"abstract":"When sufficient prior knowledge is lacking or manual annotation is difficult, solving the problem directly based on training samples of unknown category can greatly reduce the time cost. Therefore, we add unsupervised learning to the preliminary groundwork of image captioning for efficient image domain conversion to achieve batch generation of the required images. At the same time, more and more infrared images are being applied to assist decision making and environment perception. Generating more diverse and discriminative image captions in similar scenes will be effective in enhancing decision making and perception capabilities. Our infrared image caption model trained with reinforcement learning has satisfactory results both in terms of quantitative scores and in real scene tests.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"AES-12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126527716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046595
Haitao Jiang, Tuan Li, Chuang Shi
Pseudorange measurements from GNSS (Global Navigation Satellite System) receivers are seriously affected by multipath in urban environments, which greatly degrades the positioning accuracy and reliability of GNSS/Inertial Navigation System (INS)/Vision integrated system. Fault Detection and Exclusion (FDE) module is essential to improve the robustness and positioning performance of the system. Recently, GNSS/INS/Vision integration via factor graph optimization (FGO) has attracted extensive attention due to its high accuracy and robustness. As measurements from multiple epochs can be used under FGO framework, it is expected that the detection capability of faulty pseudorange measurements can be improved significantly. Meanwhile, the inclusion of visual measurements could contribute to the capability of FDE of faulty GNSS measurements. In this contribution, we present a parallel GNSS FDE method via FGO, and it calculate the test statistics of each satellite based on the residuals of GNSS measurements in a sliding window. The public GVINS-dataset "urban" were used to evaluate the performance of the parallel GNSS FDE scheme in urban canyons. Experimental results show that compared with the GNSS/INS integration, the 2D positioning accuracy in terms of Root Mean Square Error of the parallel GNSS FDE scheme used for GNSS/INS/Vision integration is improved by 33.5% in urban complex environment. Additionally, compared with the sliding window-based FDE method, for GNSS/INS integration and GNSS/INS/Vision integration, the 2D positioning accuracy is increased by 12.1% and 11.7% respectively.
{"title":"An Effective GNSS Fault Detection and Exclusion Algorithm for Tightly Coupled GNSS/INS/Vision Integration via Factor Graph Optimization","authors":"Haitao Jiang, Tuan Li, Chuang Shi","doi":"10.1109/ICARCE55724.2022.10046595","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046595","url":null,"abstract":"Pseudorange measurements from GNSS (Global Navigation Satellite System) receivers are seriously affected by multipath in urban environments, which greatly degrades the positioning accuracy and reliability of GNSS/Inertial Navigation System (INS)/Vision integrated system. Fault Detection and Exclusion (FDE) module is essential to improve the robustness and positioning performance of the system. Recently, GNSS/INS/Vision integration via factor graph optimization (FGO) has attracted extensive attention due to its high accuracy and robustness. As measurements from multiple epochs can be used under FGO framework, it is expected that the detection capability of faulty pseudorange measurements can be improved significantly. Meanwhile, the inclusion of visual measurements could contribute to the capability of FDE of faulty GNSS measurements. In this contribution, we present a parallel GNSS FDE method via FGO, and it calculate the test statistics of each satellite based on the residuals of GNSS measurements in a sliding window. The public GVINS-dataset \"urban\" were used to evaluate the performance of the parallel GNSS FDE scheme in urban canyons. Experimental results show that compared with the GNSS/INS integration, the 2D positioning accuracy in terms of Root Mean Square Error of the parallel GNSS FDE scheme used for GNSS/INS/Vision integration is improved by 33.5% in urban complex environment. Additionally, compared with the sliding window-based FDE method, for GNSS/INS integration and GNSS/INS/Vision integration, the 2D positioning accuracy is increased by 12.1% and 11.7% respectively.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123112297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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