• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1316-1319
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• 1996

This paper proposes the algorithm for the correction of a position and orientation of small object such as chip in the precise construction process. In the past, it is general to correct position and orientation of object using human sight and simple vision sensors. But recently, researches using image processing devices have been studied to improve the corrective precision of a position and orientation of object. In this piper, maximum-axis moment and p-theta algorithm are used to correct the position and orientation. Algorithm of maximum-axis moment is widely applied to hetero-object except being applied to a perfect rectangle. This is reason that moments of the X and Y-axis are equal. Therefore, being the shape of a perfect rectangle, the object is applied to other algorithm. In the light of time problem, real-time control is as important as correction of object. To solve it, we use the DSP(Digital Signal Processing) which is far more fast than PC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.504-509
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• 1995

In this paper, we developed a position correction system of industrial robot for door-chassis assembly task. With the aid of a dedicated vision system, industrial robot accomplished visually acceptable door-chassis's assembly task. The alogorithm of the position detection of notch and 2 dimesional position correction algorithm are noteworthy. The obtained algorithms were satisfatorily implemented for a real door-chassis model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.565-570
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• 1994

This work deals with finding a suitable position correction algorithm of industrial robot based on measuring gaps between door and chassis. The algorithm calculates correction quantities and then must allow visually acceptable door-chassis assembly take. The algorithm simulation is performed for a simple door-chassis model, and its effectiveness is addressed in terms of the predefined total unformity, line uniformity. In addition, the error sensitivity analysis of the rotation center of door due to the mismatch of robot grasping is performed using the algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4971-4987
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• 2019

Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.63-67
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• 2016

Recently, BLE beacons are widely used in indoor precision positioning systems because of their low battery consumption and low infrastructure cost. However, existing BLE beacon based indoor positioning algorithms are difficult to compensate for position errors due to the user's moving speed. Therefore, we proposed a position error correction algorithm that combines bounced cancellation and minimum distance maintenance algorithm with a positioning error correction method using direction vectors. Experimental results show that the proposed algorithm guarantees superior positioning performance than the existing indoor positioning algorithm and also improves the performance of position error compensation.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.42-48
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• 1996

In this paper, a position correction system of industrial robot for door-chassis assembly tast is developed in connection with the position correction algorithm shown in Part I. Tow notches and a hole of auto chassis are selected as the reference measure points and a vision based error detection algorithm is devised to measure in accuracy of less than 0.07mm. And also, the transformation between base and tool coordinates of the robot is shown to send the suitable correction quantities caaording to robot's option. The obtained algorithms were satisfactorily implemented for a real door-chassis model such that the system could accomplish visually acceptable door-chassis assembly task.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.312-318
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• 2022

Robot-based layout automation has been recently promoted for the purpose of improving productivity and quality. Marking robots have various functional demands to secure marking precision and environmental adaptability. In particular, in order to automate marking work of building structure, correction of the marking line through position recognition of rebars placed is required. Because the rebars must maintain a constant cover thickness from the formwork surface, if the rebars are out of planned position, the rebar or marking line need to be corrected to secure the cover thickness. Thus, the marking robot for structural work needs to have the function for determining the position correction of the rebar or the marking line. In order to judge the correction of marking line, it is required to measure the distance between the planned marking line and the rebar placed. Therefore, this study proposes an algorithm that can measure the distance between the planned line and the rebar, and correct marking line for the automatic operation of the marking robot. The results of this study will be utilized as a core function for unmanned operation of the marking robot and contribute to securing precise marking by reflecting construction errors.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.119-124
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• 2013

Recent increasing demand on the indoor localization requires more advanced and hybrid technology. This paper proposes an application of the hybrid indoor localization method based on a position-coded pattern that can be used with other existing indoor localization techniques such as vision, beacon, or landmark technique. To reduce the pattern-recognition error rate, the error detection and correction algorithm was applied based on Hamming code. The indoor localization experiments based on the proposed algorithm were performed by using a QCIF-grade CMOS sensor and a position-coded pattern with an area of $1.7{\times}1.7mm^2$. The experiments have shown that the position recognition error ratio was less than 0.9 % with 0.4 mm localization accuracy. The results suggest that the proposed method could be feasibly applied for the localization of the indoor mobile service robots.