• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.195-199
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• 2009

Augmented reality is a field of computer research which deals with the combination of real-world and computer-generated data, where computer graphics objects are blended into real footage in real time and it has tremendous potential in visualizing geospatial information. However, to utilize augmented reality in mobile system, many researches have undergone with GPS or marker based approaches. Localization and tracking of current position become more complex problem when it is used in indoor environments. Many proposed RF based tracking and localization. However, it does cause deployment problems of large sensors and readers. In this paper, we present a noble markerless AR approach for indoor navigation system only using a camera. We will apply this work to mobile seamless indoor/outdoor u-GIS system.

• Journal of Drive and Control
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• v.20 no.4
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• pp.1-8
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• 2023

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.120-128
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• 2012

The loop closure problem is one of the most challenging issues in the vision-based simultaneous localization and mapping community. It requires the robot to recognize a previously visited place from current camera measurements. While the loop closure often relies on visual bag-of-words based on point features in the previous works, however, in this paper we propose a line-based method to solve the loop closure in the corridor environments. We used both the floor line and the anchored vanishing point as the loop closing feature, and a two-step loop closure algorithm was devised to detect a known place and perform the global pose correction. We propose an anchored vanishing point as a novel loop closure feature, as it includes position information and represents the vanishing points in bi-direction. In our system, the accumulated heading error is reduced using an observation of a previously registered anchored vanishing points firstly, and the observation of known floor lines allows for further pose correction. Experimental results show that our method is very efficient in a structured indoor environment as a suitable loop closure solution.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.323-324
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• 2022

본 논문에서는 사람의 개입 없이 실내 환경에서 마스크 미 착용자를 스스로 발견한 후 방역수칙위반 사실에 대한 경고와 함께 마스크 착용을 권고하는 인공지능 기반의 자율주행 서비스 로봇을 개발한다. 제안한 시스템에서 로봇은 동시적 위치 추적 지도 작성 기법인 SLAM(Simultaneous Localization and Mapping)기술을 이용하여 지도를 작성한 후 사용자가 제공한 웨이포인트(Waypoint)를 기반으로 자율주행한다. 또한, YOLO(You Only Look Once) 알고리즘을 이용한 실시간 객체 인식 기술을 활용하여 보행자의 마스크 착용 여부를 판단한다. 실험을 통해 사전에 작성된 지도에 지정된 웨이포인트를 따라 로봇이 자율주행하는 것을 확인하였다. 또한, 충전소로 이동할 경우, 영상 처리 기법을 활용하여 충전소에 부착된 표식에 근접하도록 이동하여 충전이 진행됨을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.5
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• pp.367-373
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• 2016

In this paper, range measurement performance of LRF (Laser Range Finder) module and image contrast of color CCD camera are evaluated under the aerosol (high temperature steam) environments, which are simulated severe accident conditions of the LWR (Light-Water-Reactor) nuclear power plant. Data of LRF and color CCD camera are key informations, which are needed in the implementation of SLAM (Simultaneous Localization and Mapping) function for emergency response robot system to cope with urgently accidents of the nuclear power plant.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.487-488
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• 2023

전 세계적으로 도시화가 가속화됨에 따라 도시 내 차량의 수가 지속해서 증가하고 있지만 주차 공간의 부족으로 인해 도심 내 불법주차 문제가 심각해지고 있다. 또한 주차된 차량의 운전자 정보가 노출된 환경으로 인해 개인 정보 유출이 중요한 사회적 문제가 되고 있다. 따라서 본 논문에서는 불법주차 해소와 운전자 개인정보 보호를 동시에 해결하기 위한 자율주행 로봇 시스템을 제안한다. 제안한 방법에서는 정상 주차를 식별하기 방안으로 마커 인식을 적용하였고 ROS 기반 Stella N1을 사용하여 자율주행할 수 있는 로봇을 제작하였다. 또한 전화번호 없이 운전자와 연락을 취할 수 있는 메시지전달 앱을 개발하였다.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.353-360
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• 2012

This paper describes a SLAM method which estimates landmark locations and robot pose simultaneously. The particle filter can deal with nonlinearity of robot motion as well as the non Gaussian property of robot motion uncertainty and sensor error. The state to be estimated includes the locations of landmarks in addition to the robot pose. In the experiment, four beacons which transmit ultrasonic signal are used as landmarks. The robot receives the ultrasonic signals from the beacons and detects the distance to them. The method uses rang scanning sensor to build geometric feature of the environment. Since robot location and heading are estimated by the particle filter, the scanned range data can be converted to the geometric map. The performance of the method is compared with that of the deadreckoning and trilateration.