The Experts below are selected from a list of 378273 Experts worldwide ranked by ideXlab platform
Rangaprasad Arun Srivatsan - One of the best experts on this subject based on the ideXlab platform.
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a multi domain feature learning method for visual place recognition
International Conference on Robotics and Automation, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
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A Multi-Domain Feature Learning Method for Visual Place Recognition
arXiv: Robotics, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season \textit{NORDLAND} dataset, and the multi-weather \textit{GTAV} dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
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ICRA - A Multi-Domain Feature Learning Method for Visual Place Recognition
2019 International Conference on Robotics and Automation (ICRA), 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
Kweebo Sim - One of the best experts on this subject based on the ideXlab platform.
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artificial immune based swarm behaviors of distributed autonomous robotic systems
International Conference on Robotics and Automation, 2001Co-Authors: Sangjoon Sun, Dongwook Lee, Kweebo SimAbstract:We propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. For applying the immune system to DARS, a robot is regarded as a B-cell, each Environmental Condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the Environmental Condition changes, a robot selects an appropriate behavior strategy and its behavior strategy is stimulated and suppressed by other robots using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and the idiotopic network hypothesis. It is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.
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realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system
Systems Man and Cybernetics, 1999Co-Authors: Jinhyung Jun, Dongwook Lee, Kweebo SimAbstract:In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each Environmental Condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the Environmental Condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.
Peng Yin - One of the best experts on this subject based on the ideXlab platform.
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a multi domain feature learning method for visual place recognition
International Conference on Robotics and Automation, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
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A Multi-Domain Feature Learning Method for Visual Place Recognition
arXiv: Robotics, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season \textit{NORDLAND} dataset, and the multi-weather \textit{GTAV} dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
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ICRA - A Multi-Domain Feature Learning Method for Visual Place Recognition
2019 International Conference on Robotics and Automation (ICRA), 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
Chen Yin - One of the best experts on this subject based on the ideXlab platform.
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a multi domain feature learning method for visual place recognition
International Conference on Robotics and Automation, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
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A Multi-Domain Feature Learning Method for Visual Place Recognition
arXiv: Robotics, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season \textit{NORDLAND} dataset, and the multi-weather \textit{GTAV} dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
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ICRA - A Multi-Domain Feature Learning Method for Visual Place Recognition
2019 International Conference on Robotics and Automation (ICRA), 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of Environmental Conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the Environmental factors, leading to decreased accuracy decreases when Environmental Conditions change significantly, such as day versus night. To this end, we propose an end-to-end Conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the Environmental Condition-related features from those that are not. The only label required within this feature learning pipeline is the Environmental Condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant Environmental Conditions.
Dongwook Lee - One of the best experts on this subject based on the ideXlab platform.
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artificial immune based swarm behaviors of distributed autonomous robotic systems
International Conference on Robotics and Automation, 2001Co-Authors: Sangjoon Sun, Dongwook Lee, Kweebo SimAbstract:We propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. For applying the immune system to DARS, a robot is regarded as a B-cell, each Environmental Condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the Environmental Condition changes, a robot selects an appropriate behavior strategy and its behavior strategy is stimulated and suppressed by other robots using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and the idiotopic network hypothesis. It is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.
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realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system
Systems Man and Cybernetics, 1999Co-Authors: Jinhyung Jun, Dongwook Lee, Kweebo SimAbstract:In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each Environmental Condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the Environmental Condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.
