• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.217-228
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• 2024

LiDAR(Light Detection And Ranging) sensors, which play a pivotal role among cameras, RADAR(RAdio Detection And Ranging), and ultrasonic sensors for the safe operation of autonomous vehicles, can recognize and detect objects in 360 degrees. However, since LiDAR sensors use lasers to measure distance, they are vulnerable to attackers and face various security threats. In this paper, we examine several security threats against LiDAR sensors: relay, spoofing, and replay attacks, analyze the possibility and impact of physical jamming attacks, and analyze the risk these attacks pose to the reliability of autonomous driving systems. Through experiments, we show that jamming attacks can cause errors in the ranging ability of LiDAR sensors. With vehicle-to-vehicle (V2V) communication, multi-sensor fusion under development and LiDAR anomaly data detection, this work aims to provide a basic direction for countermeasures against these threats enhancing the security of autonomous vehicles, and verify the practical applicability and effectiveness of the proposed countermeasures in future research.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.397-406
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• 2022

Recently, research and development to revitalize smart construction are being actively carried out. Accordingly, 3D mapping technology that digitizes construction site is drawing attention. To create a 3D digital map for construction site a point cloud generation method based on LiDAR(Light detection and ranging) using MMS(Mobile mapping system) is mainly used. The purpose of this study is to analyze the accuracy of MMS LiDAR-based point cloud data. As a result, accuracy of MMS point cloud data was analyzed as dx = 0.048m, dy = 0.018m, dz = 0.045m on average. In future studies, accuracy comparison of point cloud data produced via UAV(Unmanned aerial vegicle) photogrammetry and MMS LiDAR should be studied.

• Spatial Information Research
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• v.13 no.2 s.33
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• pp.157-166
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• 2005

LiDAR(Light Detection And Ranging) can make terrain model where above the ground and the mixed data between SBES(Single Beam Echo Sounder) and SSS(Side Scan Sonar) can make terrain model where bottom of water. So this research suggest that how to merge data which are got ken different devices and we developed the software which can display 2D/3D graphic and water volume calculation. And we compared accuracy between the commercial software'Surfer'and LiDAR and SBES data Merging Program.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.3
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• pp.191-197
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• 2017

Light detection and ranging (LiDAR) is one of the most efficient technologies to obtain the topographic and bathymetric map of coastal zones, superior to other technologies, such as sound navigation and ranging (SONAR) and synthetic aperture radar (SAR). However, the measurement results using LiDAR are vulnerable to environmental factors. To achieve a correspondence between the acquired LiDAR data and reality, error sources must be considered, such as the water surface slope, water turbidity, and seafloor slope. Based on the knowledge of those factors' effects, error corrections can be applied. We concentrated on the effect of the seafloor slope on LiDAR waveforms while restricting other error sources. A simulation regarding in-water beam scattering was conducted, followed by an investigation of the correlation between the seafloor slope and peak timing of return waveforms. As a result, an equation was derived to correct the depth error caused by the seafloor slope.

• Korean Journal of Optics and Photonics
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• v.34 no.4
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• pp.139-150
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• 2023

Despite various light detection and ranging (LiDAR) architectures, it is very difficult to achieve long-range detection and high resolution in both vertical and horizontal directions with a wide field of view (FOV). The scanning architecture is advantageous for high-performance LiDAR that can attain long-range detection and high resolution for vertical and horizontal directions. However, a large-area photodetector (PD), which is disadvantageous for detection speed, is essentially required to secure the wide FOV. Thus we propose a PD based on the static unitary detector (STUD) technique that can operate multiple small-area PDs as a single large-area PD at a high speed. The InP/InGaAs STUD PIN-PD proposed in this paper is fabricated in various types, ranging from 1,256 ㎛×949 ㎛ using 32 small-area PDs of 1,256 ㎛×19 ㎛. In addition, we measure and analyze the noise and signal characteristics of the LiDAR receiving board, as well as the performance and sensitivity of various types of STUD PDs. Finally, the LiDAR receiving board utilizing the STUD PD is applied to a 3D scanning LiDAR prototype that uses a 1.5-㎛ master oscillator power amplifier laser. This LiDAR precisely detects long-range objects over 50 m away, and acquires high-resolution 3D images of 320 pixels×240 pixels with a diagonal FOV of 32.6 degrees simultaneously.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.849-858
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• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.403-409
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• 2003

LiDAR(Light Detection and Ranging) is considered to be a very accurate and useful tool for detection and reconstruction of ground objects. LiDAR data has information about both intensity and x,y,z position of the ground objects. LiDAR data can be collected from both first and last-return, which are called multi-return, with up to 5 different returns simultaneously. In this paper, an approach to reconstruct buildings in urban area using LiDAR multi-return data is presented. The reconstructed buildings are combined with DEM(Digital Elevation Model) produced from DSM(Digital Surface Model) in given area to implement 3D modeling. As a result, it is shown that buildings in urban area can be reconstructed and classified by the integration of the multi-return and intensity data of LiDAR.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.1-11
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• 2018

Mapping accurate spatial information is important for constructing three-dimensional (3D) spatial models and managing artificial facilities, and, especially, mapping road centerlines is necessary for constructing accurate road maps. This research developed a semi-automatic methodology for mapping road centerlines using the MMS(Mobile Mapping System) LiDAR(Light Detection And Ranging) point cloud as follows. First, the intensity image was generated from the given MMS LiDAR data through the interpolation method. Next, the line segments were extracted from the intensity image through the edge detection technique. Finally, the road centerline segments were manually selected among the extracted line segments. The statistical results showed that the generated road centerlines had 0.065 m overall accuracy but had some errors in the areas near road signs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.49-58
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• 2012

Buildings become complex and diverse with time. It is difficult to extract individual buildings using only an optical image, because they have similar spectral characteristics to objects such as vegetation and roads. In this study, we propose a method to extract building area and boundary through integrating airborne Light Detection and Ranging(LiDAR) data and aerial images. Firstly, a binary edge map was generated using Edison edge detector after applying Adaptive dynamic range linear stretching radiometric enhancement algorithm to the aerial image. Secondly, building objects on airborne LiDAR data were extracted from normalized Digital Surface Model and aerial image. Then, a temporary building areas were extracted by overlaying the binary edge map and building objects extracted from LiDAR data. Finally, some building boundaries were additionally refined considering positional accuracy between LiDAR data and aerial image. The proposed method was applied to two experimental sites for validation. Through error matrix, F-measure, Jaccard coefficient, Yule coefficient, and Overall accuracy were calculated, and the values had a higher accuracy than 0.85.