The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Norihiro Hagita - One of the best experts on this subject based on the ideXlab platform.
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combining laser Range Finders and local steered response power for audio monitoring
Intelligent Robots and Systems, 2012Co-Authors: Jani Even, Takahiro Miyashita, Carlos Toshinori Ishi, Panikos Heracleous, Norihiro HagitaAbstract:This paper presents an audio monitoring system for detecting and identifying people engaged in a conversation. The proposed method is hands-free as it uses a microphone array to acquire the sound. A particularity of the approach is the use of a laser Range finder based human tracker system. The human tracker monitors the locations of people then local steered response power is used to detect the people speaking and localize precisely their mouths. Then an audio stream is created for each person and used to perform speaker identification. Experimental results show that the use of the human tracker has several benefits compared to an audio only approach.
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automatic position calibration and sensor displacement detection for networks of laser Range Finders for human tracking
Intelligent Robots and Systems, 2010Co-Authors: Dylan F. Glas, Hiroshi Ishiguro, Takahiro Miyashita, Norihiro HagitaAbstract:Laser Range Finders are a non-invasive tool which can be used for anonymously tracking the motion of people and robots in real-world environments with high accuracy. Based on a commercial system we have developed, this paper addresses two practical issues of using networks of portable laser Range Finders in field environments. We first describe a technique for automated calibration of sensor positions and orientations, by using velocity-based matching of observed human trajectories to define constraints between the sensors. We then propose a mechanism for detecting when a sensor has been moved out of alignment, which can be used to alert an operator of the condition and automatically exclude erroneous data from tracking calculations. After describing our techniques for solving these problems, we demonstrate the effectiveness of our calibration and error detection systems in live trials with our real-time system, as well as offline tests based on scan data recorded from field trials.
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IROS - Simultaneous people tracking and localization for social robots using external laser Range Finders
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro HagitaAbstract: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.
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Simultaneous people tracking and localization for social robots using external laser Range Finders
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro HagitaAbstract: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.
Dylan F. Glas - One of the best experts on this subject based on the ideXlab platform.
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automatic position calibration and sensor displacement detection for networks of laser Range Finders for human tracking
Intelligent Robots and Systems, 2010Co-Authors: Dylan F. Glas, Hiroshi Ishiguro, Takahiro Miyashita, Norihiro HagitaAbstract:Laser Range Finders are a non-invasive tool which can be used for anonymously tracking the motion of people and robots in real-world environments with high accuracy. Based on a commercial system we have developed, this paper addresses two practical issues of using networks of portable laser Range Finders in field environments. We first describe a technique for automated calibration of sensor positions and orientations, by using velocity-based matching of observed human trajectories to define constraints between the sensors. We then propose a mechanism for detecting when a sensor has been moved out of alignment, which can be used to alert an operator of the condition and automatically exclude erroneous data from tracking calculations. After describing our techniques for solving these problems, we demonstrate the effectiveness of our calibration and error detection systems in live trials with our real-time system, as well as offline tests based on scan data recorded from field trials.
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IROS - Simultaneous people tracking and localization for social robots using external laser Range Finders
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro HagitaAbstract: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.
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Simultaneous people tracking and localization for social robots using external laser Range Finders
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro HagitaAbstract: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.
Takeo Kanade - One of the best experts on this subject based on the ideXlab platform.
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sensor placement design for object pose determination with three light stripe Range Finders
International Conference on Robotics and Automation, 1994Co-Authors: K. Kemmotsu, Takeo KanadeAbstract:The pose (position and orientation) of a polyhedral object can be determined with Range data obtained from simple light-stripe Range Finders. However, localization results are sensitive to where those Range Finders are placed in the workspace, that is, sensor placement. It is advantageous for vision tasks in a factory environment to plan optimal sensing positions off-line all at once rather than online sequentially. This paper presents a method for finding an optimal sensor placement off-line to accurately determine the pose of an object when using three light-stripe Range Finders. We evaluate a sensor placement on the basis of average performance measures such as an error rate of object recognition, recognition speed and pose uncertainty over the state space of object pose by a Monte Carlo method. An optimal sensor placement which is given a maximal score by a scalar function of the performance measures is selected by another Monte Carlo method. We emphasize that the expected performance of our system under an optimal sensor placement can be characterized completely via simulation. >
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ICRA - Sensor placement design for object pose determination with three light-stripe Range Finders
Proceedings of the 1994 IEEE International Conference on Robotics and Automation, 1Co-Authors: K. Kemmotsu, Takeo KanadeAbstract:The pose (position and orientation) of a polyhedral object can be determined with Range data obtained from simple light-stripe Range Finders. However, localization results are sensitive to where those Range Finders are placed in the workspace, that is, sensor placement. It is advantageous for vision tasks in a factory environment to plan optimal sensing positions off-line all at once rather than online sequentially. This paper presents a method for finding an optimal sensor placement off-line to accurately determine the pose of an object when using three light-stripe Range Finders. We evaluate a sensor placement on the basis of average performance measures such as an error rate of object recognition, recognition speed and pose uncertainty over the state space of object pose by a Monte Carlo method. An optimal sensor placement which is given a maximal score by a scalar function of the performance measures is selected by another Monte Carlo method. We emphasize that the expected performance of our system under an optimal sensor placement can be characterized completely via simulation. >
Hiroshi Ishiguro - One of the best experts on this subject based on the ideXlab platform.
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automatic position calibration and sensor displacement detection for networks of laser Range Finders for human tracking
Intelligent Robots and Systems, 2010Co-Authors: Dylan F. Glas, Hiroshi Ishiguro, Takahiro Miyashita, Norihiro HagitaAbstract:Laser Range Finders are a non-invasive tool which can be used for anonymously tracking the motion of people and robots in real-world environments with high accuracy. Based on a commercial system we have developed, this paper addresses two practical issues of using networks of portable laser Range Finders in field environments. We first describe a technique for automated calibration of sensor positions and orientations, by using velocity-based matching of observed human trajectories to define constraints between the sensors. We then propose a mechanism for detecting when a sensor has been moved out of alignment, which can be used to alert an operator of the condition and automatically exclude erroneous data from tracking calculations. After describing our techniques for solving these problems, we demonstrate the effectiveness of our calibration and error detection systems in live trials with our real-time system, as well as offline tests based on scan data recorded from field trials.
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IROS - Simultaneous people tracking and localization for social robots using external laser Range Finders
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro HagitaAbstract: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.
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Simultaneous people tracking and localization for social robots using external laser Range Finders
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro HagitaAbstract: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.
Hideki Hashimoto - One of the best experts on this subject based on the ideXlab platform.
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position estimation based on the target shape information using laser Range Finders for intelligent space
International Conference on Advanced Intelligent Mechatronics, 2010Co-Authors: Takeshi Sasaki, Hideki Hashimoto, Hajime Tamura, Fumihiro InoueAbstract:Intelligent Space (iSpace) is a space which has ubiquitous sensory intelligence. In iSpace, positions of objects inside the space are one of the most basic information to implement various applications. This paper describes a method to localize the target objects using distributed laser Range Finders. Since data from a laser Range finder is only distances and angles to object surfaces, we need to estimate the object center position based on its contour. To do this accurately, information of the target shape is utilized since there exist various iSpace applications where we can select the shape of the target object or attach markers of the selected shape to the object in advance. An experimental result is performed to evaluate the proposed method. Applications of the proposed method are also shown.
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calibration of laser Range Finders based on moving object tracking in intelligent space
International Conference on Networking Sensing and Control, 2009Co-Authors: Takeshi Sasaki, Hideki HashimotoAbstract:In this research, we address the calibration of laser Range Finders distributed in Intelligent Space (iSpace) which is a space with multiple embedded and networked sensors and actuators. In order to realize automated calibration, we utilize the mobile robots in the space. The mobility of mobile robots allows us to cover wide areas of the environment without placing many landmarks in the exact positions beforehand. The calibration is performed based on the positions of the mobile robots in world coordinate system and their corresponding points in local coordinate system. We also extend this approach to utilize general moving objects such as humans and not limited to mobile robots. The relative position and orientation of sensors are calculated based on the tracking result of moving objects in overlapping observable areas of different sensors. Experimental results show the validity of the proposed method.
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calibration of distributed laser Range Finders based on object tracking
IFAC Proceedings Volumes, 2009Co-Authors: Takeshi Sasaki, Hideki HashimotoAbstract:Abstract In this research, we address the calibration of laser Range Finders distributed in Intelligent Space (iSpace) which is a space with multiple embedded and networked sensors and actuators. In order to realize automated calibration, we utilize moving objects that are not limited to mobile robots in the space. Moving objects can cover wide areas of the environment so there is no need to place many landmarks in exactly known positions beforehand. The calibration is performed based on the positions of moving objects in overlapping observable areas of different sensors. The relative position and orientation of sensors are first estimated from a set of corresponding points. The global poses of the sensors are then calculated from the relative pose estimations. Experimental results show the validity of the proposed method.
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model based robot localization using onboard and distributed laser Range Finders
Intelligent Robots and Systems, 2008Co-Authors: Drazen Brscic, Hideki HashimotoAbstract:In this paper we present a method for estimating the position of mobile robots using a combination of both robotpsilas onboard sensors and sensors at fixed locations in the environment, where we use laser Range Finders as sensors. This is a situation which arises in so-called intelligent spaces, where there are both static sensors and mobile robots present. The method we present extends the robot localization methods based on occupancy grids, however here occupancy grids are used to represent not only the geometry of the environment but also that of the robot. For tracking the robot we employ a particle filter. The details of the method are given and experimental results are shown to illustrate the method.
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comparison of robot localization methods using distributed and onboard laser Range Finders
International Conference on Advanced Intelligent Mechatronics, 2008Co-Authors: Drazen Brscic, Hideki HashimotoAbstract:In this paper we discuss methods for estimating the position of objects in environments that have both distributed sensing devices as well as mobile robots equipped with sensors - intelligent spaces. The aim is to use both types of devices for the estimation. We focus on the utilization of laser Range finder devices as sensors, due to their good sensing characteristics. Our main interest here is in the localization of mobile robots and we consider two estimation methods. One is based on a heuristic determination of the center of the tracked object and utilizes a Kalman filter based estimation approach. The other method is based on geometric models of both the environment and the robot, and the position is estimated using a particle filter. The methods are described and experimental results are shown, and their comparison is given.
