• ETRI Journal
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• v.41 no.6
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• pp.782-796
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• 2019

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large-scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi-Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• The Journal of Korea Robotics Society
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• v.9 no.2
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• pp.96-103
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• 2014

Global localization is one of the essential issues for mobile robot navigation. In this study, an indoor global localization method is proposed which uses a Kinect sensor and a monocular upward-looking camera. The proposed method generates an environment map which consists of a grid map, a ceiling feature map from the upward-looking camera, and a spatial feature map obtained from the Kinect sensor. The method selects robot pose candidates using the spatial feature map and updates sample poses by particle filter based on the grid map. Localization success is determined by calculating the matching error from the ceiling feature map. In various experiments, the proposed method achieved a position accuracy of 0.12m and a position update speed of 10.4s, which is robust enough for real-world applications.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.170-177
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• 2014

This paper proposes a novel upward-looking camera-based global localization using a ceiling image map. The ceiling images obtained through the SLAM process are integrated into the ceiling image map using a particle filter. Global localization is performed by matching the ceiling image map with the current ceiling image using SURF keypoint correspondences. The robot pose is then estimated by the coordinate transformation from the ceiling image map to the global coordinate system. A series of experiments show that the proposed method is robust in real environments.

• Management & Information Systems Review
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• v.19
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• pp.17-48
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• 2006

While the existing research took approach method that uniformly divided the cultural difference between specific countries into individualism - collectivism in the dimension of comparative culture, this research defines the cultural sense of value of domestic university students and company employees in accordance with horizontalism-verticalism pattern within it to corroboratively define the diverse effects of advertisements of standardization and localization of global enterprises. Researching the difference of cultural sense of value owing to this generation of Korean university students and company employees, Korean university students showed prominent propensity of vertical-individualism value and company employees, propensity of horizontal-collectivism value. Researching comparatively the effect of advertisements for standardization and localization of global enterprises in connection with Korean university students and company employees, it turned out Korean university students prominently prefer localization advertisement in the TV advertisement of global enterprises and company employees, standardization advertisement. Especially, the difference between standardization advertisement and localization advertisement of university students and company employees was very remarkable in trademark attitude and advertisement information rather than advertisement attitude. With respect to model appearing in the advertisement, it turned out university students prefer localization advertisement where domestic models appear and company employees prefer standardization advertisement where foreign models appear.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.29-39
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• 2007

We present an implementation of particle filter algorithm for global localization and kidnap recovery of mobile robot. Firstly, we propose an algorithm for efficient particle initialization using sonar line features. And then, the average likelihood and entropy of normalized weights are used as a quality measure of pose estimation. Finally, we propose an active kidnap recovery by adding new particle set. New and independent particle set can be initialized by monitoring two quality measures. Added particle set can re-estimate the pose of kidnapped robot. Experimental results demonstrate the capability of our global localization and kidnap recovery algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.734-741
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• 2009

A new localization algorithm is proposed for a fast moving mobile robot, which utilizes only one beacon and the global features of the differential-driving mobile robot. It takes a relatively long time to localize a mobile robot with active beacon sensors since the distance to the beacon is measured by the traveling time of the ultrasonic signal. When the mobile robot is moving slowly the measurement time does not yield a high error. At a higher mobile robot speed, however, the localization error becomes too large to locate the mobile robot. Therefore, in high-speed mobile robot operations, instead of using two or more active beacons for localization, only one active beacon and the global features of the mobile robot are used to localize the mobile robot in this research. The two global features are the radius and center of the rotational motion for the differential-driving mobile robot which generally describe motion of the mobile robot and are used for the trace prediction of the mobile robot. In high speed operations the localizer finds an intersection point of this predicted trace and a circle which is centered at the beacon and has the radius of the distance between the mobile robot and the beacon. This new approach resolves the large localization error caused by the high speed of the mobile robot. The performance of the new localization algorithm has been verified through the experiments with a high-speed mobile robot.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.955-961
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• 2006

The global ultrasonic system for the self-localization of a mobile robot consists of several ultrasonic transmitters fixed at some reference positions in the global coordinates of robot environment. By activating the ultrasonic transmitters, the mobile robot is able to get the distance to the ultrasonic transmitters and compute its own position in the global coordinate. Due to the limitation on the ultrasonic signal strength and beam width as well as the environmental obstacles however, the ultrasonic signals from some generator may not be transmitted to the robot. Thus, instead of activating the all ultrasonic transmitters, it is necessary to select some ultrasonic generators to activate based on the current robot position. In this paper, we propose a selective activation algorithm for self-localization with the global ultrasonic system. The selective activation algorithm gets the meaningful ultrasonic data at every sampling instants, which results in the faster and more accurate response of the self-localization than the conventional sequential activation. Through the self-localization and path following control, we verify the effectiveness of the proposed selective activation algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.145-151
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• 2003

A global ultrasonic sensor system for self-localization of a mobile robot is proposed in this paper. The global ultrasonic sensor system consists of three or more ultrasonic transmitters fixed at some positions in the world coordinate and receivers in the moving coordinate of a mobile robot. In this global sensor system it is easy to get state vector of the mobile robot in the world coordinate from the distance information between each ultrasonic transmitter and receiver. An extended kalman filter algorithm is used to process the noisy ultrasonic signal and to estimate the state vector. In case of using several independent ultrasonic transmitters, it is necessary to avoid the cross talk among the ultrasonic waves and to synchronize between each ultrasonic transmitter and receiver. The small sized radio frequency modules are adopted to solve the cross talk and the synchronization problem Computer simulation and experiments are carried out to verify the effectiveness of the proposed ultrasonic sensor system.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.529-536
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• 2004

Autonomous navigation of an indoor mobile robot using the global ultrasonic system is presented in this paper. Since the trajectory error of the dead-reckoning navigation grows with time and distance, the autonomous navigation of a mobile robot requires to localize the current position of the robot, so that to compensate the trajectory error. The global ultrasonic system consisting of four ultrasonic generators fixed at a priori known positions in the work space and two receivers on the mobile robot has the similar structure with the well-known satellite GPS(Global Positioning System), and it is useful for the self-localization of an indoor mobile robot. The EKF(Extended Kalman Filter) algorithm for the self-localization is proposed and the autonomous navigation based on the self-localization is verified by experiments.