The Experts below are selected from a list of 238341 Experts worldwide ranked by ideXlab platform
Wolfram Burgard - One of the best experts on this subject based on the ideXlab platform.
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an analytical lidar Sensor Model based on ray path information
arXiv: Robotics, 2019Co-Authors: Alexander Schaefer, Lukas Luft, Wolfram BurgardAbstract:Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming Sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a Model of the specific Sensor. Over the past years, lidar Sensors have become a popular choice for mapping and localization. However, many common lidar Models perform poorly in unstructured, unpredictable environments, they lack a consistent physical Model for both mapping and localization, and they do not exploit all the information the Sensor provides, e.g. out-of-range measurements. In this paper, we introduce a consistent physical Model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection Model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our Model outperforms state-of-the-art Sensor Models in extensive real-world experiments.
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An Analytical Lidar Sensor Model Based on Ray Path Information
IEEE Robotics and Automation Letters, 2017Co-Authors: Alexander Schaefer, Lukas Luft, Wolfram BurgardAbstract:Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming Sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a Model of the specific Sensor. Over the past years, lidar Sensors have become a popular choice for mapping and localization. However, many common lidar Models perform poorly in unstructured, unpredictable environments, they lack a consistent physical Model for both mapping and localization, and they do not exploit all the information the Sensor provides, e.g. out-of-range measurements. In this letter, we introduce a consistent physical Model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection Model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our Model outperforms state-of-the-art Sensor Models in extensive real-world experiments.
Ryosuke Shibasaki - One of the best experts on this subject based on the ideXlab platform.
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GPS-supported visual SLAM with a rigorous Sensor Model for a panoramic camera in outdoor environments
Sensors (Switzerland), 2013Co-Authors: Yun Shi, Yulin Duan, Zhongchao Shi, Shunping Ji, Ryosuke ShibasakiAbstract:Accurate localization of moving Sensors is essential for many fields, such as robot navigation and urban mapping. In this paper, we present a framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous Sensor Model in a panoramic camera. The rigorous Model does not cause system errors, thus representing an improvement over the widely used ideal Sensor Model. The proposed SLAM does not require additional restrictions, such as loop closing, or additional Sensors, such as expensive inertial measurement units. In this paper, the problems of the ideal Sensor Model for a panoramic camera are analysed, and a rigorous Sensor Model is established. GPS data are then introduced for global optimization and georeferencing. Using the rigorous Sensor Model with the geometric observation equations of BA, a GPS-supported BA-SLAM approach that combines ray observations and GPS observations is then established. Finally, our method is applied to a set of vehicle-borne panoramic images captured from a campus environment, and several ground control points (GCP) are used to check the localization accuracy. The results demonstrated that our method can reach an accuracy of several centimetres.
Thomas C Sudhof - One of the best experts on this subject based on the ideXlab platform.
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a dual ca2 Sensor Model for neurotransmitter release in a central synapse
Nature, 2007Co-Authors: Jianyuan Sun, Zhiping P Pang, Dengkui Qin, Abigail T Fahim, Roberto Adachi, Thomas C SudhofAbstract:Ca2+-triggered synchronous neurotransmitter release is well described, but asynchronous release-in fact, its very existence-remains enigmatic. Here we report a quantitative description of asynchronous neurotransmitter release in calyx-of-Held synapses. We show that deletion of synaptotagmin 2 (Syt2) in mice selectively abolishes synchronous release, allowing us to study pure asynchronous release in isolation. Using photolysis experiments of caged Ca2+, we demonstrate that asynchronous release displays a Ca2+ cooperativity of approximately 2 with a Ca2+ affinity of approximately 44 microM, in contrast to synchronous release, which exhibits a Ca2+ cooperativity of approximately 5 with a Ca2+ affinity of approximately 38 muM. Our results reveal that release triggered in wild-type synapses at low Ca2+ concentrations is physiologically asynchronous, and that asynchronous release completely empties the readily releasable pool of vesicles during sustained elevations of Ca2+. We propose a dual-Ca2+-Sensor Model of release that quantitatively describes the contributions of synchronous and asynchronous release under conditions of different presynaptic Ca2+ dynamics.
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a dual ca2 Sensor Model for neurotransmitter release in a central synapse
Nature, 2007Co-Authors: Zhiping P Pang, Abigail T Fahim, Roberto Adachi, Thomas C SudhofAbstract:Ca2+-triggered synchronous neurotransmitter release is well described, but asynchronous release—in fact, its very existence—remains enigmatic. Here we report a quantitative description of asynchronous neurotransmitter release in calyx-of-Held synapses. We show that deletion of synaptotagmin 2 (Syt2) in mice selectively abolishes synchronous release, allowing us to study pure asynchronous release in isolation. Using photolysis experiments of caged Ca2+, we demonstrate that asynchronous release displays a Ca2+ cooperativity of ∼2 with a Ca2+ affinity of ∼44 μM, in contrast to synchronous release, which exhibits a Ca2+ cooperativity of ∼5 with a Ca2+ affinity of ∼38 μM. Our results reveal that release triggered in wild-type synapses at low Ca2+ concentrations is physiologically asynchronous, and that asynchronous release completely empties the readily releasable pool of vesicles during sustained elevations of Ca2+. We propose a dual-Ca2+-Sensor Model of release that quantitatively describes the contributions of synchronous and asynchronous release under conditions of different presynaptic Ca2+ dynamics. Neurotransmitter release at nerve terminals is triggered by an influx of calcium ions, either spontaneously or, as action potentials, in response to nerve impulses. In the latter case, release is fast (and synchronous) or delayed (asynchronous). Using the tools of genetics and electrophysiology, Sun et al. have teased apart synchronous and asynchronous releases, and propose the existence of independent calcium Sensors for the two. They also develop a quantitative Model to account for the full range of calcium-dependence of synaptic transmission. A combination of genetics and electrophysiology is used to tease apart synchronous and asynchronous releases of neurotransmitter at nerve terminals, and the existence of independent calcium Sensors is proposed. The first quantitative Model to account for the full range of calcium-dependence of synaptic transmission is also provided.
Alexander Schaefer - One of the best experts on this subject based on the ideXlab platform.
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an analytical lidar Sensor Model based on ray path information
arXiv: Robotics, 2019Co-Authors: Alexander Schaefer, Lukas Luft, Wolfram BurgardAbstract:Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming Sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a Model of the specific Sensor. Over the past years, lidar Sensors have become a popular choice for mapping and localization. However, many common lidar Models perform poorly in unstructured, unpredictable environments, they lack a consistent physical Model for both mapping and localization, and they do not exploit all the information the Sensor provides, e.g. out-of-range measurements. In this paper, we introduce a consistent physical Model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection Model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our Model outperforms state-of-the-art Sensor Models in extensive real-world experiments.
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An Analytical Lidar Sensor Model Based on Ray Path Information
IEEE Robotics and Automation Letters, 2017Co-Authors: Alexander Schaefer, Lukas Luft, Wolfram BurgardAbstract:Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming Sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a Model of the specific Sensor. Over the past years, lidar Sensors have become a popular choice for mapping and localization. However, many common lidar Models perform poorly in unstructured, unpredictable environments, they lack a consistent physical Model for both mapping and localization, and they do not exploit all the information the Sensor provides, e.g. out-of-range measurements. In this letter, we introduce a consistent physical Model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection Model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our Model outperforms state-of-the-art Sensor Models in extensive real-world experiments.
Yun Shi - One of the best experts on this subject based on the ideXlab platform.
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GPS-supported visual SLAM with a rigorous Sensor Model for a panoramic camera in outdoor environments
Sensors (Switzerland), 2013Co-Authors: Yun Shi, Yulin Duan, Zhongchao Shi, Shunping Ji, Ryosuke ShibasakiAbstract:Accurate localization of moving Sensors is essential for many fields, such as robot navigation and urban mapping. In this paper, we present a framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous Sensor Model in a panoramic camera. The rigorous Model does not cause system errors, thus representing an improvement over the widely used ideal Sensor Model. The proposed SLAM does not require additional restrictions, such as loop closing, or additional Sensors, such as expensive inertial measurement units. In this paper, the problems of the ideal Sensor Model for a panoramic camera are analysed, and a rigorous Sensor Model is established. GPS data are then introduced for global optimization and georeferencing. Using the rigorous Sensor Model with the geometric observation equations of BA, a GPS-supported BA-SLAM approach that combines ray observations and GPS observations is then established. Finally, our method is applied to a set of vehicle-borne panoramic images captured from a campus environment, and several ground control points (GCP) are used to check the localization accuracy. The results demonstrated that our method can reach an accuracy of several centimetres.
