• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.300-308
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• 2024

Urban air mobility (UAM) is emerging as a revolutionary transportation solution to urban congestion and environmental issues. Especially, electric vertical take-off and landing (eVTOL) aircraft are expected to enhance urban mobility, reduce traffic congestion, and decrease environmental pollution. However, the successful implementation and operation of UAM systems heavily rely on advanced technological infrastructure, particularly in sensor technology. Among these, 3D light detection and ranging (LiDAR) systems are essential for detecting obstacles and generating pathways in complex urban environments. This paper focuses on the challenges of developing LiDAR-based perception solutions, emphasizing the importance and performance of object detection capabilities using 3D LiDAR. It integrates LiDAR data processing algorithms and object detection methodologies to experimentally validate the effectiveness of perception solutions that contribute to the safe navigation of aircraft. This research significantly enhances the ability of aircraft to recognize and avoid obstacles effectively within urban settings.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.229-242
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• 2005

Photogrammetry is a current method of GIS data acquisition. However, as a matter of fact, a large manpower and expenditure for making detailed 3D spatial information is required especially in urban areas where various buildings exist. There are no photogrammetric systems which can automate a process of spatial information acquisition completely. On the other hand, LiDAR has high potential of automating 3D spatial data acquisition because it can directly measure 3D coordinates of objects, but it is rather difficult to recognize the object with only LiDAR data, for its low resolution at this moment. With this background, we believe that it is very advantageous to integrate LiDAR data and stereo CCD images for more efficient and automated acquisition of the 3D spatial data with higher resolution. In this research, the automatic urban object recognition methodology was proposed by integrating ultra highresolution stereo images and LiDAR data. Moreover, a method to enable more reliable and detailed stereo matching method for CCD images was examined by using LiDAR data as an initial 3D data to determine the search range and to detect possibility of occlusions. Finally, intellectual DSMs, which were identified urban features with high resolution, were generated with high speed processing.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.221-223
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• 2015

해방70년을 맞이하는 차원에서 일제강점기 동안에 조선인 강재징용으로 구축된 진지갱도(신주백, 2003)에 대한 구조물의 분포와 활용에 대한 내역을 규명하고(황석규, 2006), 일제에 의하여 자행된 국토훼손의 실상 및 만행을 확인하여 분류하는데 있다. '한반도는 일제강점기 일본 제국의 군사요새였다'(이완희, 2014)고 제시하였듯이 조사탐사의 범위는 전국을 대상으로 하며, 진지갱도지역에 대한 3-D LiDAR 기법에 의한 도면작성으로 붕괴위험에 처한 진지동굴의 분포, 형상, 내용 등의 분석으로 문화재적 측면의 보전위한 기록물 구현방안에 있다.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1416-1426
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• 2021

In the case of a scanning type multi-channel LiDAR sensor, the distance error called a walk error may occur due to a difference in received signal power. This work error causes different distance values to be output for the same object when scanning the surrounding environment based on multiple LiDAR sensors. For minimizing walk error in overlapping regions when scanning all directions using multiple sensors, to calibrate distance for each channels using convolution on external system. Four sensors were placed in the center of 6×6 m environment and scanned around. As a result of applying the proposed filtering method, the distance error could be improved by about 68% from average of 0.5125 m to 0.16 m, and the standard deviation could be improved by about 48% from average of 0.0591 to 0.030675.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.1
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• pp.1-9
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• 2019

In this paper, we propose a method of generating a 2D gray image from LiDAR 3D reflection intensity. The proposed method uses the Fully Convolutional Network (FCN) to generate the gray image from 2D reflection intensity which is projected from LiDAR 3D intensity. Both encoder and decoder of FCN are configured with several convolution blocks in the symmetric fashion. Each convolution block consists of a convolution layer with $3{\times}3$ filter, batch normalization layer and activation function. The performance of the proposed method architecture is empirically evaluated by varying depths of convolution blocks. The well-known KITTI data set for various scenarios is used for training and performance evaluation. The simulation results show that the proposed method produces the improvements of 8.56 dB in peak signal-to-noise ratio and 0.33 in structural similarity index measure compared with conventional interpolation methods such as inverse distance weighted and nearest neighbor. The proposed method can be possibly used as an assistance tool in the night-time driving system for autonomous vehicles.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.113-120
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• 2016

The necessity of maritime sector for continuous management, accurate and update location information such as seabed shape and location, research on airborne LiDAR bathymetry surveying techniques are accelerating. Airborne LiDAR systems consist of a scanner and GPS/INS. The location accuracy of 3D point data obtained by a LiDAR system is determined by external orientation parameters. However, there are problems in the synchronization between sensors should be performed due to a variety of sensor combinations and arrangement. To solve this issue, system calibration should be conducted. Therefore, this study evaluates the system verification methods, processes, and operation techniques.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.3-12
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• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.