• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.299-305
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• 2021

In this study, the task of robotic tidy-up is to clean the current environment up exactly like a target image. To perform a tidy-up task using a robot, it is necessary to estimate the pose of various objects and to classify the objects. Pose estimation requires the CAD model of an object, but these models of most objects in daily life are not available. Therefore, this study proposes an algorithm that uses point cloud and PCA to estimate the pose of objects without the help of CAD models in cluttered environments. In addition, objects are usually detected using a deep learning-based object detection. However, this method has a limitation in that only the learned objects can be recognized, and it may take a long time to learn. This study proposes an image matching based on few-shot learning and Siamese network. It was shown from experiments that the proposed method can be effectively applied to the robotic tidy-up system, which showed a success rate of 85% in the tidy-up task.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.54-61
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• 2022

Ship is a huge system including a variety of pipe arrangements. Pipes are installed according to the design layout, however the end poistion of pipes are not well matched owing to its measurement and construction errors. In this situation, the customized pipe fitting is frequently designed to connect with both pipes, the position of which are manually measured. This paper focused that these two coordinates are measured by point cloud from RGBD sensor and the relative transformation induced by positional and orientational differences is calculated by inverse kinematics in robotics theory. Therefore, the result applies for the methodology of the pipe connection design. The pipe coordinate that is estimated by the matching and the probabilistic RANSAC method will be verified by experiments. The kinematic design parameters are computationally calculated by using the minimum degree of freedom that connects both pipe coordinates.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.597-602
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• 2016

In image guided surgery, a patient registration process is a critical process for the successful operation, which is required to use pre-operative images such as CT and MRI during operation. Though several patient registration methods have been studied, we concentrate on one method that utilizes 3D surface measurement data in this paper. First, a hand-held 3D surface measurement device measures the surface of the patient, and secondly this data is matched with CT or MRI data using optimization algorithms. However, generally used ICP algorithm is very slow without a proper initial location and also suffers from local minimum problem. Usually, this problem is solved by manually providing the proper initial location before performing ICP. But, it has a disadvantage that an experience user has to perform the method and also takes a long time. In this paper, we propose a method that can accurately find the proper initial location automatically. The proposed method finds the proper initial location for ICP by converting 3D data to 2D curvature images and performing image matching. Curvature features are robust to the rotation, translation, and even some deformation. Also, the proposed method is faster than traditional methods because it performs 2D image matching instead of 3D point cloud matching.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.293-300
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• 2020

The purpose of our sensor system is to transparentize the large hydraulic manipulators of a six-ton dual arm excavator from the operator camera view. Almost 40% of the camera view is blocked by the manipulators. In other words, the operator loses 40% of visual information which might be useful for many manipulator control scenarios such as clearing debris on a disaster site. The proposed method is based on a 3D reconstruction technology. By overlaying the camera image from front top of the cabin with the point cloud data from RGB-D (red, green, blue and depth) cameras placed at the outer side of each manipulator, the manipulator-free camera image can be obtained. Two additional algorithms are proposed to further enhance the productivity of dual arm excavators. First, a color correction algorithm is proposed to cope with the different color distribution of the RGB and RGB-D sensors used on the system. Also, the edge overlay algorithm is proposed. Although the manipulators often limit the operator's view, the visual feedback of the manipulator's configurations or states may be useful to the operator. Thus, the overlay algorithm is proposed to show the edge of the manipulators on the camera image. The experimental results show that the proposed transparentization algorithm helps the operator get information about the environment and objects around the excavator.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.388-396
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• 2011

Sun tracking system is the most important subsystem in parabolic dish type solar thermal power plant, since it determines the amount of thermal energy to be collected, thus affects the efficiency of solar thermal power plant most significantly. Various types of sun tracking systems are currently used. Among them, use of photo sensors to located the sun(which is called sensor type) and use of astronomical algorithm to compute the sun position(which is called program type) are two of the mostly used methods. Recently some uses CCD sensor, like CCD camera, which is called image processing type sun tracking system. This work is concerned with the analysis of sun tracking performance of various types of sun tracking systems currently used in the parabolic dish type solar thermal power plant. We first developed a sun tracking error measurement system. Then, we evaluate the performance of five different types of sun tracking systems, sensor type, program type, hybrid type(use of sensor and computed sun position simultaneously), tracking error compensated program type and image processing type. Experimentally obtained data shows that the tracking error compensated program type sun tracking system is very effective and could provide a good sun tracking performance. Also the data obtained shows that the performance of sensor type sun tracking system is being affected by the cloud significantly, while the performance of a program type sun tracking system is being affected by the sun tracking system's mechanical and installation errors very much. Finally image processing type sun tracking system can provide accurate sun tracking performance, but costs more and requires more computational time.

• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.8-15
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• 2020

Due to the advance of artificial intelligence, wide use of RPA(Robotic Process Automation) became inevitable. The purpose of this study is to seek cost management optimization based on RPA which has automatic collection of cost information, timeliness and flexibility. The cost management system based on RPA will be able to optimize and improve the cost management process through the cross-system of cost information recognition and the cloud platform. Following the review of previous researches on the benefit of the RPA-related technology along with the investigation on the problems of current cost management system, this study will suggest a way to adopt RPA to optimize cost management system for the implement of strategic management accounting to support management decision making.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.560-563
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• 2016

The changes in the medical and healthcare are started from the digital technology. The new field of digital healthcare has started fused with existing healthcare, medical technology, and digital technology. It can increase the service effect and reduce healthcare costs by applying ICT skills such as ICBM(Internet of Things, Cloud, Big data and Mobile), artificial intelligence, robotics, virtual, augmented reality, and wearable devices to healthcare services including healthcare, disease management. Recently there has been grafted an artificial intelligence technologies such as AlphaGo of Google and Watson of IBM onto the healthcare area. In this study, we analyze the main technology, ecosystem, platforms for digital healthcare, and lastly future changes in health care services and issues of digital healthcare.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.425-433
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• 2020

Three-dimensional data has been used for different applications such as robotics, building reconstruction, and so on. 3D data can be generated from an optical camera or a laser scanner. Especially, a wearable multi-sensor system including the above-mentioned sensors is an optimized structure that can overcome the drawbacks of each sensor. After finding the geometric relationships between sensors, georeferencing of the datasets acquired from the moving system, should be carried out. Especially, in an indoor environment, error propagation always causes problem in the georeferencing process. To improve the accuracy of this process, other sources of data were used to combine with LiDAR (Light Detection and Ranging) data, and various registration methods were also tested to find the most suitable way. More specifically, this paper proposed a new process of NDT (Normal Distribution Transform) to register the LiDAR point cloud, with additional information from other sensors. For real experiment, a wearable mapping system was used to acquire datasets in an indoor environment. The results showed that applying the new process of NDT and combining LiDAR data with IMU (Inertial Measurement Unit) information achieved the best result with the RMSE 0.063 m.

• Journal of KIBIM
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• v.10 no.1
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• pp.1-8
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• 2020

In managing building facilities, spatial information is the basic data for decision making. However, the method of acquiring spatial information is not easy. In many cases, the site and drawings are often different due to changes in facilities and time after construction. In this case, the site data should be scanned to obtain spatial information. The scan data actually contains spatial information, which is a great help in making space related decisions. However, to obtain scan data, an expensive LiDAR (Light Detection and Ranging) device must be purchased, and special software for processing data obtained from the device must be available.Recently, SLAM (Simultaneous localization and mapping), an advanced map generation technology, has been spreading in the field of robotics. Using SLAM, 3D spatial information can be obtained quickly in real time without a separate matching process. This study develops and tests whether SLAM technology can be used to obtain spatial information for facility management. This draws considerations for developing a SLAM device for real-time remote scanning for facility management. However, this study focuses on the system development method that acquires spatial information necessary for facility management through SLAM technology. To this end, we develop a prototype, analyze the pros and cons, and then suggest considerations for developing a SLAM system.