The Experts below are selected from a list of 1951197 Experts worldwide ranked by ideXlab platform
Klaus Dietmayer - One of the best experts on this subject based on the ideXlab platform.
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a random finite set approach for dynamic occupancy grid maps with real time Application
The International Journal of Robotics Research, 2018Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well-established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s envi...
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a random finite set approach for dynamic occupancy grid maps with real time Application
The International Journal of Robotics Research, 2018Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well-established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s envi...
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a random finite set approach for dynamic occupancy grid maps with real time Application
arXiv: Robotics, 2016Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a Real-Time Application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
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a random finite set approach for dynamic occupancy grid maps with real time Application
arXiv: Robotics, 2016Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a Real-Time Application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
Lizhe Wang - One of the best experts on this subject based on the ideXlab platform.
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cross layer multi cloud real time Application qos monitoring and benchmarking as a service framework
IEEE International Conference on Cloud Computing Technology and Science, 2019Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Chang Liu, Fethi A Rabhi, Lizhe WangAbstract:Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, Application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical Applications that leverage various cloud platforms. Such Applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud Applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual Application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks Application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.
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Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
IEEE Transactions on Cloud Computing, 2015Co-Authors: Khalid Alhamazani, Karan Mitra, Fethi Rabhi, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Lizhe WangAbstract:Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and networking equipment) and software (databases, Application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical Applications that leverage various cloud platforms. Such Applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud Applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual Application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks Application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.
Dominik Nuss - One of the best experts on this subject based on the ideXlab platform.
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a random finite set approach for dynamic occupancy grid maps with real time Application
The International Journal of Robotics Research, 2018Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well-established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s envi...
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a random finite set approach for dynamic occupancy grid maps with real time Application
The International Journal of Robotics Research, 2018Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well-established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s envi...
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a random finite set approach for dynamic occupancy grid maps with real time Application
arXiv: Robotics, 2016Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a Real-Time Application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
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a random finite set approach for dynamic occupancy grid maps with real time Application
arXiv: Robotics, 2016Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a Real-Time Application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
Khalid Alhamazani - One of the best experts on this subject based on the ideXlab platform.
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cross layer multi cloud real time Application qos monitoring and benchmarking as a service framework
IEEE International Conference on Cloud Computing Technology and Science, 2019Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Chang Liu, Fethi A Rabhi, Lizhe WangAbstract:Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, Application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical Applications that leverage various cloud platforms. Such Applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud Applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual Application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks Application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.
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Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
IEEE Transactions on Cloud Computing, 2015Co-Authors: Khalid Alhamazani, Karan Mitra, Fethi Rabhi, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Lizhe WangAbstract:Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and networking equipment) and software (databases, Application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical Applications that leverage various cloud platforms. Such Applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud Applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual Application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks Application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.
Stephan Reuter - One of the best experts on this subject based on the ideXlab platform.
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a random finite set approach for dynamic occupancy grid maps with real time Application
The International Journal of Robotics Research, 2018Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well-established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s envi...
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a random finite set approach for dynamic occupancy grid maps with real time Application
The International Journal of Robotics Research, 2018Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well-established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s envi...
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a random finite set approach for dynamic occupancy grid maps with real time Application
arXiv: Robotics, 2016Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a Real-Time Application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
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a random finite set approach for dynamic occupancy grid maps with real time Application
arXiv: Robotics, 2016Co-Authors: Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, Klaus DietmayerAbstract:Grid mapping is a well established approach for environment perception in robotic and automotive Applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a Real-Time Application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
