The Experts below are selected from a list of 3966 Experts worldwide ranked by ideXlab platform

Horst-michael Gross - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Automatic calibration of a stationary network of laser range finders by matching movement trajectories
    2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a Global Coordinate System. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • AVSS - Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
    2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a Global Coordinate System, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • Automatic calibration of a stationary network of laser range finders by matching movement trajectories
    2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a Global Coordinate System. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
    2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a Global Coordinate System, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

Norihiro Hagita - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Simultaneous people tracking and localization for social robots using external laser range finders
    2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009
    Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita
    Abstract:

    Robust localization of robots and reliable tracking of people are both critical requirements for the deployment of service robots in real-world environments. In crowded public spaces, occlusions can impede localization using on-board sensors. At the same time, teams of service robots working together need to share the locations of people and other robots on the same Global Coordinate System in order to provide services efficiently. To solve this problem, our approach is to use an infrastructure of sensors embedded in the environment to provide an inertial reference frame and wide-area coverage. Based on a people-tracking System we have previously established which uses laser range finders to track people's trajectories, we have developed a technique to localize a team of service robots on a shared Global Coordinate System. Each robot's odometry data is associated with the observed trajectory of an entity detected by the laser tracking System, and Kalman filters are used to correct rotational offsets between the robots' individual Coordinate Systems and the Global reference frame. We present our data association and pose correction algorithms and show results demonstrating the performance of our System in a shopping arcade.

  • Simultaneous people tracking and localization for social robots using external laser range finders
    2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009
    Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita
    Abstract:

    Robust localization of robots and reliable tracking of people are both critical requirements for the deployment of service robots in real-world environments. In crowded public spaces, occlusions can impede localization using on-board sensors. At the same time, teams of service robots working together need to share the locations of people and other robots on the same Global Coordinate System in order to provide services efficiently. To solve this problem, our approach is to use an infrastructure of sensors embedded in the environment to provide an inertial reference frame and wide-area coverage. Based on a people-tracking System we have previously established which uses laser range finders to track people's trajectories, we have developed a technique to localize a team of service robots on a shared Global Coordinate System. Each robot's odometry data is associated with the observed trajectory of an entity detected by the laser tracking System, and Kalman filters are used to correct rotational offsets between the robots' individual Coordinate Systems and the Global reference frame. We present our data association and pose correction algorithms and show results demonstrating the performance of our System in a shopping arcade.

Konrad Schenk - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Automatic calibration of a stationary network of laser range finders by matching movement trajectories
    2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a Global Coordinate System. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • AVSS - Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
    2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a Global Coordinate System, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • Automatic calibration of a stationary network of laser range finders by matching movement trajectories
    2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a Global Coordinate System. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
    2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a Global Coordinate System, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

Dylan F. Glas - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Simultaneous people tracking and localization for social robots using external laser range finders
    2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009
    Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita
    Abstract:

    Robust localization of robots and reliable tracking of people are both critical requirements for the deployment of service robots in real-world environments. In crowded public spaces, occlusions can impede localization using on-board sensors. At the same time, teams of service robots working together need to share the locations of people and other robots on the same Global Coordinate System in order to provide services efficiently. To solve this problem, our approach is to use an infrastructure of sensors embedded in the environment to provide an inertial reference frame and wide-area coverage. Based on a people-tracking System we have previously established which uses laser range finders to track people's trajectories, we have developed a technique to localize a team of service robots on a shared Global Coordinate System. Each robot's odometry data is associated with the observed trajectory of an entity detected by the laser tracking System, and Kalman filters are used to correct rotational offsets between the robots' individual Coordinate Systems and the Global reference frame. We present our data association and pose correction algorithms and show results demonstrating the performance of our System in a shopping arcade.

  • Simultaneous people tracking and localization for social robots using external laser range finders
    2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009
    Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita
    Abstract:

    Robust localization of robots and reliable tracking of people are both critical requirements for the deployment of service robots in real-world environments. In crowded public spaces, occlusions can impede localization using on-board sensors. At the same time, teams of service robots working together need to share the locations of people and other robots on the same Global Coordinate System in order to provide services efficiently. To solve this problem, our approach is to use an infrastructure of sensors embedded in the environment to provide an inertial reference frame and wide-area coverage. Based on a people-tracking System we have previously established which uses laser range finders to track people's trajectories, we have developed a technique to localize a team of service robots on a shared Global Coordinate System. Each robot's odometry data is associated with the observed trajectory of an entity detected by the laser tracking System, and Kalman filters are used to correct rotational offsets between the robots' individual Coordinate Systems and the Global reference frame. We present our data association and pose correction algorithms and show results demonstrating the performance of our System in a shopping arcade.

Klaus Debes - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Automatic calibration of a stationary network of laser range finders by matching movement trajectories
    2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a Global Coordinate System. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • AVSS - Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
    2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a Global Coordinate System, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • Automatic calibration of a stationary network of laser range finders by matching movement trajectories
    2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a Global Coordinate System. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

  • Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
    2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
    Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael Gross
    Abstract:

    Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a Global Coordinate System is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a Global Coordinate System, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.