The Experts below are selected from a list of 1407 Experts worldwide ranked by ideXlab platform
Horst-michael Gross - One of the best experts on this subject based on the ideXlab platform.
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Monocular Obstacle Detection for Real-world Environments
2016Co-Authors: Erik Einhorn, Christof Schroeter, Horst-michael GrossAbstract:Abstract. In this paper, we present a feature based approach for monocular scene reconstruction based on extended Kalman filters (EKF). Our method processes a sequence of images taken by a single camera mounted in front of a mobile robot. Using different techniques we are able to produce a precise reconstruction that is free from outliers and therefore can be used for reliable obstacle detection and avoidance. In real-world field-tests we show that the presented approach is able to detect obstacles that can not be seen by other sensors, such as Laser-Range-Finders. Furthermore, we show that visual obstacle detection combined with a Laser Range finder can increase the detection rate of obstacles considerably allowing the au-tonomous use of mobile robots in complex public and home environments.
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Automatic calibration of a stationary network of Laser Range Finders by matching movement trajectories
2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael GrossAbstract: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.
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Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012Co-Authors: Konrad Schenk, Alexander Kolarow, Markus Eisenbach, Klaus Debes, Horst-michael GrossAbstract: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.
Akihisa Ohya - One of the best experts on this subject based on the ideXlab platform.
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Laser Reflection Intensity and Multi-Layered Laser Range Finders for People Detection
2020Co-Authors: Alexander Carballo, Akihisa Ohya, Shin &aposAbstract:Abstract-Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory. In this work we introduce the usage of reflection intensity data of Laser Range Finders (LRF) arRanged in multiple layers for people detection. We use supervised learning to train strong classifiers including intensity-based features. Concretely, we propose a calibration method for Laser intensity and introduce new intensity-based features for people detection which are combined with Range-based features in a strong classifier using supervised learning. We provide experimental results to evaluate the effectiveness of these features. This work is an step towards of our main research project of developing a social autonomous mobile robot acting as member of a people group
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reliable people detection using Range and intensity data from multiple layers of Laser Range Finders on a mobile robot
International Journal of Social Robotics, 2011Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Reliable people detection is an important task in several areas like security, intelligent environments and human robot interaction. People detection does not depend only upon separation of static environment objects from those showing motion (hopefully humans), a reliable system should be able to detect static people even in cluttered environments.
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people detection using Range and intensity data from multi layered Laser Range Finders
Intelligent Robots and Systems, 2010Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Effective detection of people is a basic requirement for robot coexistence in human environments. In our previous work [1] we proposed a method for people detection and position estimation using multiple layers of Laser Range Finders (LRF) in a mobile robot. We extend our work by introducing Laser reflection intensity as a novel feature for people detection, achieving significant improvement of detection rates. In concrete, we propose a method for calibration of Laser intensity data, a method for segment separation using Laser intensity, and introduce two new intensity-based features for people detection: the variance of Laser intensity and the variance of intensity differences. We present experimental results that confirm the effectiveness of our multi-layered detection method including Laser intensity.
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Laser reflection intensity and multi layered Laser Range Finders for people detection
Robot and Human Interactive Communication, 2010Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory.
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people detection using double layered multiple Laser Range Finders by a companion robot
7th IEEE International Conference on Multi-Sensor Integration and Fusion IEEE MFI 2008, 2009Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Successful detection and tracking of people is a basic requirement to achieve a robot symbiosis in people daily life. Specifically, a mobile robot designed to follow people needs to keep track of people position through time, for it defines the robot’s position and trajectory.
S Yuta - One of the best experts on this subject based on the ideXlab platform.
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fusion of double layered multiple Laser Range Finders for people detection from a mobile robot
International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008Co-Authors: Alexander Carballo, Akihisa Ohya, S YutaAbstract:This work proposes a new method people detection and position estimation from a mobile robot by fusion of multiple Laser Range Finders arRanged in two layers. Sensors facing opposite directions in a single row (layer) are fused to produce 360deg scan data of robotpsilas surroundings, then data from every layer is further fused to create a 3D model of people and from there their position. The main problem of our research is an autonomous mobile robot acting as member of a people group moving in public areas, simple and accurate people detection and tracking is an important requirement. We present experimental results of fusion steps and people detection in an indoor environment.
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security door system using human tracking method with Laser Range Finders
International Conference on Mechatronics and Automation, 2007Co-Authors: Jae Hoon Lee, Yongshik Kim, Bong Keun Kim, Kohtaro Ohba, Hirohiko Kawata, Akihisa Ohya, S YutaAbstract:Recently, tracking and counting people are requested for the security purpose. In this paper, we propose a method to track and count people with Laser Range Finders. A method to extract the walking human from the sensor data is introduced. Multi-target model and Kalman filter based estimation are employed to track the human movement and count the number of people. The proposed method is applied to a novel system to monitor the entrance area and to filter out the trespasser who enters the door without identification. Experiments for various cases are performed to verify the usefulness of developed proto-type system.
Alexander Carballo - One of the best experts on this subject based on the ideXlab platform.
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Laser Reflection Intensity and Multi-Layered Laser Range Finders for People Detection
2020Co-Authors: Alexander Carballo, Akihisa Ohya, Shin &aposAbstract:Abstract-Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory. In this work we introduce the usage of reflection intensity data of Laser Range Finders (LRF) arRanged in multiple layers for people detection. We use supervised learning to train strong classifiers including intensity-based features. Concretely, we propose a calibration method for Laser intensity and introduce new intensity-based features for people detection which are combined with Range-based features in a strong classifier using supervised learning. We provide experimental results to evaluate the effectiveness of these features. This work is an step towards of our main research project of developing a social autonomous mobile robot acting as member of a people group
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reliable people detection using Range and intensity data from multiple layers of Laser Range Finders on a mobile robot
International Journal of Social Robotics, 2011Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Reliable people detection is an important task in several areas like security, intelligent environments and human robot interaction. People detection does not depend only upon separation of static environment objects from those showing motion (hopefully humans), a reliable system should be able to detect static people even in cluttered environments.
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people detection using Range and intensity data from multi layered Laser Range Finders
Intelligent Robots and Systems, 2010Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Effective detection of people is a basic requirement for robot coexistence in human environments. In our previous work [1] we proposed a method for people detection and position estimation using multiple layers of Laser Range Finders (LRF) in a mobile robot. We extend our work by introducing Laser reflection intensity as a novel feature for people detection, achieving significant improvement of detection rates. In concrete, we propose a method for calibration of Laser intensity data, a method for segment separation using Laser intensity, and introduce two new intensity-based features for people detection: the variance of Laser intensity and the variance of intensity differences. We present experimental results that confirm the effectiveness of our multi-layered detection method including Laser intensity.
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Laser reflection intensity and multi layered Laser Range Finders for people detection
Robot and Human Interactive Communication, 2010Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory.
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people detection using double layered multiple Laser Range Finders by a companion robot
7th IEEE International Conference on Multi-Sensor Integration and Fusion IEEE MFI 2008, 2009Co-Authors: Alexander Carballo, Akihisa Ohya, Shinichi YutaAbstract:Successful detection and tracking of people is a basic requirement to achieve a robot symbiosis in people daily life. Specifically, a mobile robot designed to follow people needs to keep track of people position through time, for it defines the robot’s position and trajectory.
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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object tracking based calibration of poses of distributed Laser Range Finders for intelligent space
SICE journal of control measurement and system integration, 2009Co-Authors: Takeshi Sasaki, Hideki HashimotoAbstract:This paper presents an automated calibration method for Laser Range Finders distributed in the environment. The Laser Range finder has recently been considered to be one of the most useful sensors ...
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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.
