• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1987-1992
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• 2010

This paper deals with the method of using the three dimensional characteristic information to classify the front environment in travelling by using the images captured by a CCD camera equipped on a mobile robot. The images detected by the three dimensional characteristic information is divided into the part of obstacles, the part of corners, and th part of doorways in a corridor. In designing the travelling path of a mobile robot, these three situations are used as an important information in the obstacle avoidance and optimal path computing. So, this paper proposes the method of deciding the travelling direction of a mobile robot with using input images based upon the suggested algorithm by preprocessing, and verified the validity of the image information which are detected as obstacles by the analysis through neural network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.121-130
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• 2009

In this paper, Gaussian probability distribution model based multi-sensor data fusion algorithm is proposed for a vehicular location awareness system. Conventional vehicular location awareness systems are operated by GPS (Global Positioning System). However, the conventional system is not working in the indoor of building or urban area where the receiver is difficult to receive the signal from satellites. A method which is combined GPS and UWB (Ultra Wide-Band) has developed to improve this problem. However, vehicular is difficult to receive seamless location information since the measurement systems by both GPS and UWB convert the vehicle's movement information separately at each sensor. In this paper, normalized probability distribution model based Hybrid UWB/GPS is proposed by utilizing GPS location data and UWB sensor data. Therefore the proposed system provides information with seamless and location flexible properties. The proposed system tested by Ubisense and Asen GPS in the $12m{\times}8m$ outdoor environments. As a result, the proposed system has improved performance for accurateness and connection ability between devices to support various CNS (Car Navigation System).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.160-172
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• 2018

The method of estimating a position using a GPS has been applied to various fields including a navigation system of an automobile. However, since it is difficult to measure GPS signals indoors, it is difficult to locate specific objects indoors such as a building or factory. To overcome these limitations, this study proposes a system for object location estimation based on Bluetooth5 for the management of materials in factories. The object position estimation system consists of a Bluetooth signal generator, a receiver, and a database server. A signal generator based on Bluetooth Low Energy(BLE) is attached to the material and a receiver is appropriately arranged inside the factory. In this study, we propose "Diagonal Survey Method", a 4 - axis survey algorithm using four receivers to reduce the error of existing trilateration method. The proposed algorithm showed good performance compared to the conventional trilateration and we verified the effectiveness of the proposed system and algorithm by performing the experiment by installing the system in the factory.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.73-82
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• 2010

Door is an important object to understand given environment and it could be used to distinguish with corridors and rooms. Doors are widely used natural landmark in mobile robotics for localization and navigation. However, almost algorithm for door recognition with camera is difficult real-time application because feature extraction and matching have heavy computation complexity. This paper proposes a method to recognize a door in corridor. First, we extract distinguished lines which have high possibility to comprise of door using Hough transformation. Then, we detect candidate of door region by applying previously extracted lines to first-stage visual fuzzy system. Finally, door regions are determined by verifying knob region in candidate of door region suing second-stage visual fuzzy system.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.173-182
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• 2005

In this paper, we propose a landmark recognition method which is irrelevant to the camera viewpoint on the navigation for localization. Features in previous research is variable to camera viewpoint, therefore due to the wealth of information, extraction of visual landmarks for positioning is not an easy task. The proposed method in this paper, has the three following stages; first, extraction of features, second, learning and recognition, third, matching. In the feature extraction stage, we set the interest areas of the image. where we extract the corner points. And then, we extract features more accurate and resistant to noise through statistical analysis of a small eigenvalue. In learning and recognition stage, we form robust feature models by testing whether the feature model consisted of five corner points is an invariant feature irrelevant to viewpoint. In the matching stage, we reduce time complexity and find correspondence accurately by matching method using similarity evaluation function and Graham search method. In the experiments, we compare and analyse the proposed method with existing methods by using various indoor images to demonstrate the superiority of the proposed methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.165-170
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• 2015

The topic of this paper is the advanced absolute altitude determination for 3-D positioning using barometric altimeter and the reference station. Barometric altimeter does not provide absolute altitude because atmosphere pressure always varies over the time and geographical location. Also, since Global Navigation Satellites system such as GPS, GLONASS has geometric error, the altitude information is not available. It is the reason why we suggested the new method to improve the altitude accuracy. This paper shows 3-D positioning algorithm using absolute altitude determination method and evaluates the algorithm by real field tests. We used an accurate altitude from RTK system in Seoul as a reference data and acquired the differential value of pressure data between a reference station and a mobile station equipped in low cost barometric altimeter. In addition, the performance and advantage of the proposed method was evaluated by 3-D experiment analysis of PNS and CNS. We expect that the proposed method can expand 2-D positioning system 3-D position determination system simply and this 3-D position determination technique can be very useful for the workers in the field of fire-fighting and construction.

• Tunnel and Underground Space
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• v.32 no.3
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• pp.231-242
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• 2022

In this study, we developed a robot operating system (ROS)-based autonomous driving robot that estimates the robot's position in underground mines and drives and returns through multiple waypoints. Autonomous driving robots utilize SLAM (Simultaneous Localization And Mapping) technology to generate global maps of driving routes in advance. Thereafter, the shape of the wall measured through the LiDAR sensor and the global map are matched, and the data are fused through the AMCL (Adaptive Monte Carlo Localization) technique to correct the robot's position. In addition, it recognizes and avoids obstacles ahead through the LiDAR sensor. Using the developed autonomous driving robot, experiments were conducted on indoor experimental sites that simulated the underground mine site. As a result, it was confirmed that the autonomous driving robot sequentially drives through the multiple waypoints, avoids obstacles, and returns stably.

• Computers and Concrete
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• v.33 no.5
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• pp.535-544
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• 2024

In the rapidly advancing landscape of computer vision (CV) technology, there is a burgeoning interest in its integration with the construction industry. Camera calibration is the process of deriving intrinsic and extrinsic parameters that affect when the coordinates of the 3D real world are projected onto the 2D plane, where the intrinsic parameters are internal factors of the camera, and extrinsic parameters are external factors such as the position and rotation of the camera. Camera pose estimation or extrinsic calibration, which estimates extrinsic parameters, is essential information for CV application at construction since it can be used for indoor navigation of construction robots and field monitoring by restoring depth information. Traditionally, camera pose estimation methods for cameras relied on target objects such as markers or patterns. However, these methods, which are marker- or pattern-based, are often time-consuming due to the requirement of installing a target object for estimation. As a solution to this challenge, this study introduces a novel framework that facilitates camera pose estimation using standardized materials found commonly in construction sites, such as concrete forms. The proposed framework obtains 3D real-world coordinates by referring to construction materials with certain specifications, extracts the 2D coordinates of the corresponding image plane through keypoint detection, and derives the camera's coordinate through the perspective-n-point (PnP) method which derives the extrinsic parameters by matching 3D and 2D coordinate pairs. This framework presents a substantial advancement as it streamlines the extrinsic calibration process, thereby potentially enhancing the efficiency of CV technology application and data collection at construction sites. This approach holds promise for expediting and optimizing various construction-related tasks by automating and simplifying the calibration procedure.