• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.64.4-64
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• 2001

Localization is the key role in controlling the Mobile Robot. In this papers, a development of the sensor fusion algorithm for controling UTV(Unmanned Tracked Vehicle) is presented. The multi-sensocial dead-rocking subsystem is established based on the optimal filtering by first fusing heading angle reading from a magnetic compass, a rate-gyro and two encoders mouned on the robot wheels, thereby computing the deat-reckoned location. These data and the position data provoded by LBL system are fused together by means of an extended Kalman filter. This algorithm is proved by simulation studies.

• ETRI Journal
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• v.29 no.2
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• pp.189-200
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• 2007

This paper addresses the localization and navigation problem in the movement of service robots by using invisible two dimensional barcodes on the floor. Compared with other methods using natural or artificial landmarks, the proposed localization method has great advantages in cost and appearance since the location of the robot is perfectly known using the barcode information after mapping is finished. We also propose a navigation algorithm which uses a topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls, and many static obstacles. The proposed algorithm also has the advantage that errors which occur in each node are mutually independent and can be compensated exactly after some navigation using barcodes. Simulation and experimental results were performed to verify the algorithm in the barcode environment, showing excellent performance results. After mapping, it is also possible to solve the kidnapped robot problem and to generate paths using topological information.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1933-1938
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• 2003

A covariance matrix is a tool that expresses odometry uncertainty of the wheeled mobile robot. The covariance matrix is a key factor in various localization algorithms such as Kalman filter, topological matching and so on. However it is not easy to acquire an accurate covariance matrix because we do not know the real states of the robot. Up to the authors knowledge, there seems to be no established result on the covariance matrix estimation for the odometry. In this paper, we propose a new method which can estimate the covariance matrix from empirical data. It is based on the PC-method and shows a good estimation ability. The experimental results validate the performance of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.293-299
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• 2009

This paper discusses an autonomous navigation system for urban environments. For the localization of the robot, EKF (Extended Kalman Filter) algorithm is used with odometry, angle sensor, and DGPS (Differential Global Positioning System) measurement. Especially in an urban environment, DGPS is often blocked by buildings and trees and the resulting inaccurate positioning prevents the robot from safe and reliable navigation. In addition to the global information from DGPS, the local information of the curb on the roadway is used to track a route when the global DGPS information is inaccurate. For this purpose, curb detection algorithm is developed and implemented in the developed navigation algorithm. Four different types of navigation strategies are developed and they are switched to adapt to different localization conditions according to the availability of DGPS and the existence of the curbs on the roadway. The experimental results show that the designed switching strategy improves the navigation performance adapting to the environment conditions.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.529-531
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• 2004

For localization, it is very important for an autonomous mobile robot to be able to recognize indoor environment and match an object it detect to an object within a map developed either online or offline. Given the map defining the locations of geometric beacons like wall and comer existing in the robot operation environment, this paper presents a stereo ultrasonic sensor based method that can be conveniently used in recognizing the geometric beacons. The stereo ultrasonic sensor used in the experiment consists of an ultrasonic transmitter and two ultrasonic receivers placed symmetrically about the transmitter. Experiment shows that the proposed method is more efficient in recognizing wall and coner than the conventional method of using multiple number of transmitter-receiver pairs.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.330-336
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• 1996

An real-time active beacon localization system for mobile robots is developed and implemented. This system permits the estimation of robot positions when detecting light sources by PSD(Position Sensitive Detector) sensor which are placed sparsely over the robots work space as beacons(or landmarks). An LSE(Least Square Estimation) method is introduced to calibrate the internal parameters of a model for the beacon and robot position. The proposed system has two operational modes of position estimation. One is the initial position calculation by the detection of two or more light sources positions of which are known. The other is the continuous position compensation that calculates the position and heading of the robot using the IEKF(Iterated Extended Kalman Filter) applied to the beacon and dead-reckoning data. Practical experiments show that the estimated position obtained by this system is precise enough to be useful for the navigation of robots.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1812_1813
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• 2009

This paper presents an accurate indoor localization method of a mobile robot using ultrasonic sensors. The coordinates of mobile robot are calculated by using trilateration which is using the distance between the transmitter and receiver. At this time, the distances can't be accurately calculated by containing noise. We propose Extended Kalman Filter(EKF) to improve estimation accuracy. The performance of proposed EKF is evaluated by simulation program. As a result, we confirm that the errors in estimate of mobile robot's position are eliminated from measured distance.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.379-384
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• 2013

We propose a new global positioning method for the indoor mobile robots. The multiple indoor lights fixed in ceiling are used as the landmarks of positioning system. The ceiling images are acquired by the fisheye lens camera mounted on the moving robot. The position and orientation of the lights are extracted by binarization and labeling techniques. Also the boundary lines between ceiling and walls are extracted to identify the order of each light. The robot position is then calculated from the extracted position and known position of the lights. The proposed system can increase the accuracy and reduce the computation time comparing with the other positioning methods using natural landmark. Experimental results are presented to show the performance of the method.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.4
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• pp.33-40
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• 2005

This paper represents the stable path recognition by the ultrasonic sensor which gathers navigation environments and the monocular image sensor which generates the self localization information of mobile robot. The proposed ultrasonic sensor and vision camera system recognizes the target and extracts parameters for generating the world map and self localization. Therefore, this paper has developed an indoor mobile robot and has stably demonstrated in a corridor environment.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.485-493
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• 2004

Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two position estimation methods are developed in this paper, one using a single optical flow sensor and a second using two optical sensors. The first method can accurately estimate position under ideal conditions and also when wheel slip perpendicular to the axis of the wheel occurs. The second method can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, a method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where wheel slip occurs.