• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.210-216
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• 2017

In the paper we propose and implement a new indoor localization system where the techniques of magnetic field based fingerprinting and pedestrian dead reckoning are combined. First, we determine a target's location by comparing acquired magnetic field values with a magnetic field map containing pre-collected field values at different locations and choosing the location having the closest value. As the target moves, we use pedestrian dead reckoning to estimate the expected moving path, reducing the maximum positioning error of the initial location. The system eliminates the problem of localization error accumulation in pedestrian dead reckoning with the help of the fingerprinting and does not require Wi-Fi AP infrastructure, enabling cost-effective localization solution.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.516-523
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• 2009

Recently, the applications of unmanned robots are increasing in various fields including surveillance and reconnaissance, planet exploration and disaster relief. To perform their missions with success, the robots should be able to evaluate terrain's characteristics quantitatively and identify traversable regions to progress toward a goal using mounted sensors. Recently, the authors have proposed techniques that extract terrain information and analyze traversability for off-road navigation of an unmanned robot. In this paper, we examine the use of 3D world models(terrain maps) to classify obstacle and safe terrain for increasing the reliability of the proposed techniques. A world model is divided into several patches and each patch is classified as belonging either to an obstacle or a non-obstacle using three types of metrics. The effectiveness of the proposed method is verified on real terrain maps.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.119-124
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• 2009

In this paper, we propose a novel approach to estimating the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the input-output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.451-457
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• 2012

Recently, as the use of real time traffic information of a car navigation system increases rapidly with the development of Intelligent Transportation Systems (ITS), path search is getting more important. Previous algorithms, however, are mostly for the shortest distance searching and provide route information using static distance and time information. Thus they could not provide the most optimal route at the moment which changes dynamically according to traffic. Accordingly, in this study, Semantic Shortest Path algorithm with Reduction ratio & Distance(SSP_RD) is proposed to solve this problem. Additionally, a routing model based on velocity reduction ratio and distance and a dynamic route link map are proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1044-1050
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• 2014

Robots used for pipe inspection have been studied for a long time and many mobile mechanisms have been proposed to achieve inspection tasks within pipelines. Localization is an important factor for an inpipe robot to perform successful autonomous operation. However, sensors such as GPS and beacons cannot be used because of the unique characteristics of inpipe conditions. In this paper, an inpipe localization algorithm based on elbow detection is presented. By processing the projected marker images of laser pointers and the attitude and heading data from an IMU, the odometer module of the robot determines whether the robot is within a straight pipe or an elbow and minimizes the integration error in the orientation. In addition, an off-line positioning algorithm has been performed with forward and backward estimation and Procrustes analysis. The experimental environment has consisted of several straight pipes and elbows, and a map of the pipeline has been constructed as the result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3373-3389
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• 2022

Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.279-280
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• 2022

도로를 주행 중인 운전자들은 비상용 차량이 접근할 때 경광등 혹은 백미러를 통해 확인하고, 도로의 상황을 파악한 후에서야 길을 양보한다. 그러나 비상 차량이 접근하는 걸 뒤늦게 인지하거나, 도로의 상황이 복잡하여 차선을 변경하다 접촉사고가 발생할 수도 있고, 또한 진행할 수 있는 도로를 미리 확보하지 못하여 비상 차량이 정해진 시간 내에 도착하지 못하는 경우가 자주 발생하고 있다. 이러한 일을 개선하기 위해서 본 논문에서는 안드로이드, GPS 기술을 이용한 앱을 통해 비상 차량이 일반 차량에 일정 거리 이내에 접근할 경우에 미리 알람을 보내고 이를 사전에 방지, 및 예방할 수 있는 앱을 개발한다. 안드로이드 스튜디오를 사용하였고, Firebase를 이용해 DB및 서버를 구축하였다.

• Ocean Systems Engineering
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• v.14 no.2
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• pp.101-114
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• 2024

Seafloor sediment mapping is an essential research topic in shallow coastal waters, especially in port development, benthic habitat mapping, and underwater communications. The seafloor sediments can be interpreted by collecting sediment samples directly in the field using a grab sampler or corer. Another method is optical, especially using underwater cameras and videos. Both methods each have weaknesses in terms of area coverage (mechanic) and accurate positioning (optic). The latest technology used to overcome it is the acoustic method (echosounder) with Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning. Therefore, in this study will propose the classification of seafloor sediments in coastal waters using acoustic method that is Multibeam Echosounder (MBES) multi-frequency with five frequency (200 kHz, 250 kHz, 300 kHz, 350 kHz, and 400 kHz). In this study, the deep neural network (DNN) used the bathymetric multi frequency, bathymetric difference inters frequencies, and bathymetric features from 5 (five) frequencies as input layer and 4 (four) sediment types in 74 (seventy-four) sample sediment as output layer to make a seafloor sediment map. Results of sediment mapping using the DNN method show an overall accuracy of 71.6% (significant) and a kappa coefficient of 0.59 (moderate). The distribution of seafloor sediment in the study area is mainly silt (41.6%), followed by clayey sand (36.6%), sandy silt (14.2%), and silty sand (7.5%).

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.530-540
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• 2005

Virtual colonoscopy is an easy and fast method to reconstruct the shape of colon and diagnose tumors inside the colon based on computed tomography images. This is a non-invasive method, which resolves weak points of previous invasive methods. The path for virtual colonoscopy should be generated rapidly and accurately for clinical examination. However, previous methods are computationally expensive because the data structure such as distance map should be constructed in the preprocessing and positions of all the points of the path needs to be calculated. In this paper, we propose the automatic path generation method based on visibility to decrease path generation time. The proposed method does not require preprocessing and generates small number of control points representing the Path instead of all points to generate the path rapidly. Also, our method generates the path based on visibility so that a virtual camera moves smoothly and a comfortable and accurate path is calculated for virtual navigation. Also, our method can be used for general virtual navigation of various kinds of pipes.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.115-125
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• 2017

In current, as the release of national base map with an equivalent scale and accuracy for the whole territory areas in South Korea, rapid spatial information industry such as national land development, GIS, and car navigation are used in a variety of spatial information industry as decision making method, and a lot of research and policies are proposed for the wide expansion of spatial information industry. For this, as of 2013, it contributes to the latest trend of spatial information field in order to solve the problems for the latest trend of spatial information, replacing modification of base maps as dividing the whole territory to zone with policy transformation by ordinary modifications. Therefore, this paper evaluates the possibility of modification and renewal of national base maps(scale: 1:5,000) using drones which currently get the limelight from a variety of research fields and industries. In particular, as a result of overlapping orthophoto, 3D point clouds extracted from images acquired by low-cost drones, and digital maps which are applied for the tasks of modification and renewal, it presents 0.2m precision and 0.1m accuracy. This means that drone-based photorgammetry technique can be fully utilized in the tasks of digital map modification and renewal because it conforms the error range of work regulation in making the national base maps(scale 1: 5000).