• 제어로봇시스템학회논문지
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• 제15권12호
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• pp.1246-1253
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• 2009

To be high efficient for a navigation of unmanned ground vehicle, it must be able to distinguish between safe and hazardous regions in its immediate environment. We present an advanced method using laser range finder for building global 2D digital maps that include environment information. Laser range finder is used for mapping of obstacles and driving environment in the 2D laser plane. Rotary encoders are used for localization of UGV. The main contributions of this research are the development of an algorithm for global 2D map building and it will turn a UGV navigation based on map matching into a possibility. In this paper, a map building algorithm will be introduced and an assessment of algorithm reliability is judged at an each environment.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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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.

• 로봇학회논문지
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• 제7권2호
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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.

• 한국지능시스템학회논문지
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• 제21권4호
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• pp.442-448
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• 2011

이동 로봇의 위치인식 기술을 위하여 SLAM(Simultaneous Localization and Mapping)에 관한 많은 연구가 진행되고 있다. 본 논문에서는 시야각이 넓은 어안렌즈를 장착한 단일 카메라를 사용하여 천장의 특징점을 이용한 자기위치 인식에 관한 방안을 제시한다. 여기서는 어안렌즈 기반의 비전 시스템이 가지는 왜곡 영상의 보정, SIFT(Scale Invariant Feature Transform) 기반의 강인한 특징점을 추출하여 이전 영상과 이동한 영상과의 정합을 통해 최적화된 영역 함수를 도출하는 과정, 그리고 기하학적 적합모델 설계 등을 제시한다. 제안한 방법을 실험실 환경 및 복도 환경에 적용하여 그 유용성을 확인한다.

• 한국컴퓨터정보학회논문지
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• 제18권10호
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• pp.55-61
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• 2013

패킷헤더의 중복된 필드를 제거하여 헤더의 사이즈를 줄이는 방법을 헤더압축이라고 할 때, 헤더에서 연속된 패킷간 일정 수치만큼씩 고정적으로 증가되는 Dynamic필드를 어떻게 고효율로 압축할 수 있느냐가 헤더압축의 중요한 이슈라고 할 수 있다. RTP프로토콜의 헤더 사이즈를 압축하는 기존의 방법으로 RFC2507, RFC3095 그리고 ROHC와 E-ROHC 등의 기법이 있다. 본 논문에서는 RTP 패킷의 Dynamic필드인 TS필드를 BCB(Basic Compression Bits) 기본 비트로 압축하거나 또는 NCB(Negotiation Compression Bits, BCB + 추가적인 비트) 비트로 압축하는 기존 방법보다 향상된 방법(Enhanced Method)을 제안하였다. 여기에서 제안한 새로운 향상된 헤더압축 기법의 성능을 검증하기 위해, 제안한 방식의 비디오패킷을 대상으로 Visual SLAM을 사용하여 시뮬레이션 하였다.

• 로봇학회논문지
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• 제16권3호
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• pp.189-198
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• 2021

In this paper, we introduce the software/hardware system that can reliably calculate the distance from sensor to the model regardless of point cloud density. As the 3d point cloud map is widely adopted for SLAM and computer vision, the accuracy of point cloud map is of great importance. However, the 3D point cloud map obtained from Lidar may reveal different point cloud density depending on the choice of sensor, measurement distance and the object shape. Currently, when measuring map accuracy, high reflective bands are used to generate specific points in point cloud map where distances are measured manually. This manual process is time and labor consuming being highly affected by Lidar sparsity level. To overcome these problems, this paper presents a hardware design that leverage high intensity point from three planar surface. Furthermore, by calculating distance from sensor to the device, we verified that the automated method is much faster than the manual procedure and robust to sparsity by testing with RGB-D camera and Lidar. As will be shown, the system performance is not limited to indoor environment by progressing the experiment using Lidar sensor at outdoor environment.

• 로봇학회논문지
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• 제16권3호
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.273-275
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• 2009

The currently used methods to get this information include corneal reflection, electro-oculograph, and so on. In this paper, we propose an algorithm that is developed for the reflection of pupil by infrared. I assume that the pupil is a perfect circle in the captured eye images. The method to recognize an existing image has many data throughput very much. And the time to deal with an image data is very long. So, we did not use the camera. However we studied the algorithm to pursue the pupil to a mapping technique.

• 전자공학회논문지B
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• 제31B권8호
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• pp.1-10
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• 1994

In this paper load redistribution algorithm to allow fault-tolerance by redistributing the workload of n failure nodes to the remaining good nodes in distributed systems are investigated. To evaluate the efficiency of the algorithms a simulation model of algorithms is developed using SLAM II simulation language. The job arrival rate service rate failure and repair rate of nodes and communication delay time due to load migraion are used as parameters. The result of the simulation shows that the job arrival rate failure and repair rate of nodes do not affected on the relative efficiency of algorithms. If the communication delay time is greater than average job processing time algorithm B is better. Otherwise algorithm C is superior to the others.

• 로봇학회논문지
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• 제18권3호
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• pp.260-270
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• 2023

We introduce a novel Visual Inertial Odometry (VIO) algorithm designed to improve the performance of thermal-inertial odometry. Thermal infrared image, though advantageous for feature extraction in low-light conditions, typically suffers from a high noise level and significant information loss during the 8-bit conversion. Our algorithm overcomes these limitations by approximating a 14-bit raw pixel histogram into a Gaussian mixture model. The conversion method effectively emphasizes image regions where texture for visual tracking is abundant while reduces unnecessary background information. We incorporate the robust learning-based feature extraction and matching methods, SuperPoint and SuperGlue, and zero velocity detection module to further reduce the uncertainty of visual odometry. Tested across various datasets, the proposed algorithm shows improved performance compared to other state-of-the-art VIO algorithms, paving the way for robust thermal-inertial odometry.