The Experts below are selected from a list of 18723 Experts worldwide ranked by ideXlab platform
Michael M. Zavlanos - One of the best experts on this subject based on the ideXlab platform.
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Controlling a robotic stereo camera under Image Quantization noise
The International Journal of Robotics Research, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through...
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Controlling a Robotic Stereo Camera Under Image Quantization Noise
arXiv: Robotics, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures Image Quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
Charles Freundlich - One of the best experts on this subject based on the ideXlab platform.
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Controlling a robotic stereo camera under Image Quantization noise
The International Journal of Robotics Research, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through...
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Controlling a Robotic Stereo Camera Under Image Quantization Noise
arXiv: Robotics, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures Image Quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
Philippos Mordohai - One of the best experts on this subject based on the ideXlab platform.
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Controlling a robotic stereo camera under Image Quantization noise
The International Journal of Robotics Research, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through...
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Controlling a Robotic Stereo Camera Under Image Quantization Noise
arXiv: Robotics, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures Image Quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
Alex Zihao Zhu - One of the best experts on this subject based on the ideXlab platform.
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Controlling a robotic stereo camera under Image Quantization noise
The International Journal of Robotics Research, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through...
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Controlling a Robotic Stereo Camera Under Image Quantization Noise
arXiv: Robotics, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures Image Quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
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Controlling a robotic stereo camera under Image Quantization noise
The International Journal of Robotics Research, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through...
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Controlling a Robotic Stereo Camera Under Image Quantization Noise
arXiv: Robotics, 2017Co-Authors: Charles Freundlich, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, Michael M. ZavlanosAbstract:In this paper, we address the problem of controlling a mobile stereo camera under Image Quantization noise. Assuming that a pair of Images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures Image Quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.