The Experts below are selected from a list of 35229 Experts worldwide ranked by ideXlab platform
Jae-bok Song - One of the best experts on this subject based on the ideXlab platform.
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Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots
Advanced Robotics, 2012Co-Authors: Se-jin Lee, Dong-woo Cho, Jae-bok SongAbstract:Abstract Not all line or point Features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle Feature clouds on Salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar Salient Feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent Feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar Salient Feature structure for EKF...
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A new sonar Salient Feature structure for EKF-based SLAM
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, 2010Co-Authors: Seung Hwan Lee, Jae-bok SongAbstract:Not all line or point Features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity. We present a new sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle Feature clouds on Salient convex objects by sonar data association. The centroid of each circle cloud, called a sonar Salient Feature, is used as a natural landmark for EKF-based SLAM. After completing initial exploration in an unknown environment, SLAM-able areas with sonar Salient Features can be defined, and cylindrical objects are placed conveniently at weak SLAM-able areas as a supplemental environmental saliency to enhance SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar Salient Feature structure for EKF-based SLAM.
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IROS - A new sonar Salient Feature structure for EKF-based SLAM
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Se-jin Lee, Jae-bok SongAbstract:Not all line or point Features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity. We present a new sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle Feature clouds on Salient convex objects by sonar data association. The centroid of each circle cloud, called a sonar Salient Feature, is used as a natural landmark for EKF-based SLAM. After completing initial exploration in an unknown environment, SLAM-able areas with sonar Salient Features can be defined, and cylindrical objects are placed conveniently at weak SLAM-able areas as a supplemental environmental saliency to enhance SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar Salient Feature structure for EKF-based SLAM.
Hong Liu - One of the best experts on this subject based on the ideXlab platform.
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a Salient Feature and scene semantics based attention model for human tracking on mobile robots
International Conference on Robotics and Automation, 2010Co-Authors: Hong LiuAbstract:It is a great challenge to perform robust tracking for a mobile robot owing to dynamic environments. Also, fast motion or abrupt jerk of the robotic camera poses a severe threat for continuous tracking. To address these problems, a novel attention model is proposed motivated by human attention mechanism which consists of low level Salient Feature and high level scene semantics. The low level layer extracts color and motion Feature to obtain combined Feature probability map. In semantic level, the ADM(attention distribution map) is computed by applying an attenuation function on the combined Feature map which is motivated by human's foveal vision. The object position is found using CAMSHIFT algorithm in ADM. And this layer also generates a region-based SSG(scene semantics graph). When robot moves abnormally, the model detects candidate regions in color saliency map and then attention shifts from one region to the next and check it by elastically matching SSG until the target is recovered. Experiments in several kinds of environments give promising results and show that this model is robust for mobile robotic tracking. When camera moves steadily, a little fast or even jerks very abruptly, it can keep continuous tracking.
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ICRA - A Salient Feature and scene semantics based attention model for human tracking on mobile robots
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Hong LiuAbstract:It is a great challenge to perform robust tracking for a mobile robot owing to dynamic environments. Also, fast motion or abrupt jerk of the robotic camera poses a severe threat for continuous tracking. To address these problems, a novel attention model is proposed motivated by human attention mechanism which consists of low level Salient Feature and high level scene semantics. The low level layer extracts color and motion Feature to obtain combined Feature probability map. In semantic level, the ADM(attention distribution map) is computed by applying an attenuation function on the combined Feature map which is motivated by human's foveal vision. The object position is found using CAMSHIFT algorithm in ADM. And this layer also generates a region-based SSG(scene semantics graph). When robot moves abnormally, the model detects candidate regions in color saliency map and then attention shifts from one region to the next and check it by elastically matching SSG until the target is recovered. Experiments in several kinds of environments give promising results and show that this model is robust for mobile robotic tracking. When camera moves steadily, a little fast or even jerks very abruptly, it can keep continuous tracking.
Se-jin Lee - One of the best experts on this subject based on the ideXlab platform.
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Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots
Advanced Robotics, 2012Co-Authors: Se-jin Lee, Dong-woo Cho, Jae-bok SongAbstract:Abstract Not all line or point Features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle Feature clouds on Salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar Salient Feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent Feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar Salient Feature structure for EKF...
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ekf based slam using sonar Salient Feature and line Feature for mobile robots
Journal of the Korean Society for Precision Engineering, 2011Co-Authors: Youngjin Heo, Jonghwan Lim, Se-jin LeeAbstract:Not all line or point Features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point Features with previously registered Feature. Confused line and point Features in cluttered environments leads to poor SLAM performance. We introduce a sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line Feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.
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IROS - A new sonar Salient Feature structure for EKF-based SLAM
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Se-jin Lee, Jae-bok SongAbstract:Not all line or point Features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity. We present a new sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle Feature clouds on Salient convex objects by sonar data association. The centroid of each circle cloud, called a sonar Salient Feature, is used as a natural landmark for EKF-based SLAM. After completing initial exploration in an unknown environment, SLAM-able areas with sonar Salient Features can be defined, and cylindrical objects are placed conveniently at weak SLAM-able areas as a supplemental environmental saliency to enhance SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar Salient Feature structure for EKF-based SLAM.
Yu-jin Zhang - One of the best experts on this subject based on the ideXlab platform.
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Salient Feature selection for visual concept learning
Lecture Notes in Computer Science, 2005Co-Authors: Lei Zhang, Yu-jin ZhangAbstract:Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative Features for an image category and meanwhile reduce noisy Features, a three-step Salient Feature selection strategy is proposed. In the Feature selection stage, Salient patches are first detected and clustered. Then the region of dominance and Salient entropy measures are calculated to reduce non-common Salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed Salient Feature selection approach is very effective in image classification and visual concept learning.
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PCM (1) - Salient Feature selection for visual concept learning
Advances in Multimedia Information Processing - PCM 2005, 2005Co-Authors: Lei Zhang, Yu-jin ZhangAbstract:Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative Features for an image category and meanwhile reduce noisy Features, a three-step Salient Feature selection strategy is proposed. In the Feature selection stage, Salient patches are first detected and clustered. Then the region of dominance and Salient entropy measures are calculated to reduce non-common Salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed Salient Feature selection approach is very effective in image classification and visual concept learning.
Seung Hwan Lee - One of the best experts on this subject based on the ideXlab platform.
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A new sonar Salient Feature structure for EKF-based SLAM
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, 2010Co-Authors: Seung Hwan Lee, Jae-bok SongAbstract:Not all line or point Features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity. We present a new sonar Feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle Feature clouds on Salient convex objects by sonar data association. The centroid of each circle cloud, called a sonar Salient Feature, is used as a natural landmark for EKF-based SLAM. After completing initial exploration in an unknown environment, SLAM-able areas with sonar Salient Features can be defined, and cylindrical objects are placed conveniently at weak SLAM-able areas as a supplemental environmental saliency to enhance SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar Salient Feature structure for EKF-based SLAM.
