• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.111-127
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• 2023

In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.88-89
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• 2022

라이다 센서는 최근 다양한 산업에서 3 차원 물체의 식별을 위해 사용되고 있다. 주요 시설이나 전시관의 경계를 위해서 스캔형 라이더는 넓은 범위의 감지를 할 수 있지만 입력 데이터가 많아 물체의 식별과 관련된 연산이 오래 걸리는 문제를 가진다. 이러한 문제를 해결하기 위해서 제안하는 방법에서는 라이다 데이터를 간략화 하고 감지 이벤트를 통해 사용자들에게 알려줄 수 있도록 하는 알고리즘을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.301-302
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• 2022

최근 지상 통로에 차량 통행 공간과 보행자 통행 공간이 분리되어있지 않은 공원형 아파트가 증가하고 있다. 이로 인해 택배 차량의 아파트 단지 안으로 진입을 통제하는 아파트가 늘어나고 있다. 현재는 이러한 상황에서 택배기사들이 직접 손수레를 끌고 아파트 안으로 들어가거나, 수령인이 직접 아파트 입구에서 택배를 수령하는 방법으로 문제를 해결 해 왔다. 본 논문은 이러한 불편함을 개선하기 위해 아파트 입구에서 집 앞까지 인공지능 기술과 카메라, 라이다센서를 이용하여 자율주행으로 택배를 운반 해줄 수 있는 '자율주행 택배 운반 로봇' 기술을 제안한다. 기존의 사람이 직접 택배를 집 앞까지 운반하는 방식이 아닌 자율주행 로봇을 이용한 방식으로 택배기사들의 과로로 인한 사고를 예방하고, 아파트 입주민들의 불편도 줄어들 것이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.848-849
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• 2023

본 논문은 로봇의 실내 환경에서의 자율성을 높이기 위한 SLAM과 내비게이션 방법을 제시한다. 2D LiDAR와 카메라를 이용하여 위치를 인식하고 사람과 장애물을 의미론적으로 검출하며, ICP와 RTAB-map, YOLOv3를 통합하여 Semantic Map을 생성하고 실내 환경에서 자율성을 유지한다. 이 연구를 통해 로봇이 복잡한 환경에서도 높은 수준의 자율성을 유지할 수 있는지 확인하고자 한다.

• ETRI Journal
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• v.37 no.3
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• pp.606-616
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• 2015

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.313-320
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• 2018

The importance of sensors on a self-driving vehicle has rising since it act as eyes for the vehicle. Lidar sensors based on laser technology tend to yield better image quality with more laser channels, thus, it has higher detection accuracy for obstacles, pedistrians, terrain, and other vechicles. However, incorporating more laser channels results higher unit price more than ten times, and this is a major drawback for using high channel lidar sensors on a vehicle for actual consumer market. To come up with this drawback, we propose a method of integrating multiple low channel, low cost lidar sensors acting as one high channel sensor. The result uses four 16 channels lidar sensors with small form factor acting as one bulky 64 channels sensor, which in turn, improves vehicles cosmetic aspects and helps widespread of using the lidar technology for the market.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.181-188
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• 2012

The most recent, Digital photogrammetric camera, Airborne LiDAR and GPS/INS same sensors are used to acquire spatial information of various kinds in the field of aerial survey. In addition, Direct Georeferencing technology has been widely utilized with digital photogrammetric camera and GPS/INS. However, the sensor Calibration to be performed according to the combination of various sensors is followed by problems. Most of all, boresight calibration of integrated sensors is a critical element in the mapping process when using direct georeferencing or using the GPS/INS aerotriangulation. The establishment of a national test-bed in Sejong-si for aerial sensor calibration is absolutely necessary to solve this problem. And accurate calibration with used to integration of GPS/INS by aerotriangulation of aerial imagery was necessary for determination of system parameters, evaluation of systematic errors. Also, an investigation of efficient method for Direct georeferencing to determine the exterior orientation parameters and assessment of geometric accuracy of integrated sensors are performed.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.131-139
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• 2006

IKONOS images have the perspective geometry in CCD sensor line like aerial images with central perspective geometry. So the occlusion by buildings, terrain or other objects exist in the image. It is difficult to detect the occlusion with RPCs(rational polynomial coefficients) for ortho-rectification of image. Therefore, in this study, we detected the occlusion areas in IKONOS images using the nominal collection elevation/azimuth angle and restored the hidden areas using another stereo images, from which the rue ortho image could be produced. The algorithm's validity was evaluated using the geometric accuracy of the generated ortho image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.397-402
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• 2019

Recently, with the emergence of autonomous vehicles and the increasing interest in safety, a variety of research has been being actively conducted to precisely estimate the position of a vehicle by fusing sensors. Previously, researches were conducted to determine the location of moving objects using GNSS (Global Navigation Satellite Systems) and/or IMU (Inertial Measurement Unit). However, precise positioning of a moving vehicle has lately been performed by fusing data obtained from various sensors, such as LiDAR (Light Detection and Ranging), on-board vehicle sensors, and cameras. This study is designed to enhance kinematic vehicle positioning performance by using feature-based recognition. Therefore, an analysis of the required precision of the observations obtained from the images has carried out in this study. Velocity and attitude observations, which are assumed to be obtained from images, were generated by simulation. Various magnitudes of errors were added to the generated velocities and attitudes. By applying these observations to the positioning algorithm, the effects of the additional velocity and attitude information on positioning accuracy in GNSS signal blockages were analyzed based on Kalman filter. The results have shown that yaw information with a precision smaller than 0.5 degrees should be used to improve existing positioning algorithms by more than 10%.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.