• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.43-62
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• 2017

Mobile pulse scanning Light Detection And Ranging (LIDAR) are essential components of intelligent vehicles capable of autonomous travel. Obstacle detection functions of autonomous vehicles require very low failure rates. With the increasing number of autonomous vehicles equipped with scanning LIDARs to detect and avoid obstacles and navigate safely through the environment, the probability of mutual interference becomes an important issue. The reception of foreign laser pulses can lead to problems such as ghost targets or a reduced signal-to-noise ratio. This paper will show the probability that any two scanning LIDARs will interfere mutually by considering spatial and temporal overlaps. We have conducted four experiments to investigate the occurrence of the mutual interference between scanning LIDARs. These four experimental results introduced the effects of mutual interference and indicated that the interference has spatial and temporal locality. It is hard to ignore consecutive mutual interference on the same line or the same angle because it is possible the real object not noise or error. It may make serious faults because the obstacle detection functions of autonomous vehicle rely on heavily the scanning LIDAR.

• Korean Journal of Optics and Photonics
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• v.27 no.4
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• pp.133-142
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• 2016

This paper presents the design and simulation of a three-dimensional pixel-by-pixel scanning light detection and ranging (LIDAR) system with a microelectromechanical system (MEMS) scanning mirror and direct sequence optical code division multiple access (DS-OCDMA) techniques. It measures a frame with $848{\times}480$ pixels at a refresh rate of 60 fps. The emitted laser pulse waves of each pixel are coded with DS-OCDMA techniques. The coded laser pulse waves include the pixel's position in the frame, and a checksum. The LIDAR emits the coded laser pulse waves periodically, without idle listening time to receive returning light at the receiver. The MEMS scanning mirror is used to deflect and steer the coded laser pulse waves to a specific target point. When all the pixels in a frame have been processed, the travel time is used by the pixel-by-pixel scanning LIDAR to generate point cloud data as the measured result.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.93-96
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• 2007

LIDAR is a relatively new technological tool that can be used to accurately georeference terrain features, and also is becoming an important 3D mapping tool in GIS. In this study it is described the capabilities of terrestrial LIDAR that was used to build a 3D terrain model of extremely steep rock face, along with the useful data and examples of contributions terrestrial lidar has made to outcrop studies. For this, High-resolution terrestrial lidar acquisition, processing, interpretation are discussed and applied to mapping of geological surfaces in three dimensions. We expected that lidar is a tool with which we can improve our current field methods and quantify the observations geologists make.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1681-1690
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• 2020

A scanning lidar system has been developed. The system has two wavelength observation channels of 532 and 1064 nm and is capable of 360-degree horizontal scanning observation. In addition, an analysis method that can classify the measured particle as an indicator of coarse-mode particle (PM2.5-10) and an indicator of fine-mode particles (PM2.5) and calculate the mass concentration of each has been developed by using the backscatter coefficient at two wavelengths. It was applied to the data calculated by observation. The mass concentrations of PM10 and PM2.5, which showed a distribution of 22-110 ㎍/㎥ and 7-78 ㎍/㎥, respectively, were successfully calculated in the Ulsan Onsan Industrial Complex using the developed scanning lidar system. The analyzed results showed similar values to the mass concentrations measured on the ground around the lidar observation area, and it was confirmed that high concentrations of 80-110 ㎍/㎥ and 60-78 ㎍/㎥ were measured at points discharged from factories, respectively.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.241-243
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were ${\pm}$8.1㎝, ${\pm}$24.7㎝, ${\pm}$35.9㎝, respectively.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.451-463
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• 2022

Whenever a mineshaft accidentally collapses, speedy risk assessment is both required and crucial. But onsite safety diagnosis by humans is reportedly difficult considering the additional risk of collapse of the unstable mineshaft. Generally, drones equipped with high-speed lidar sensors can be used for such inspection. However, the drone technology is restrictively applicable at very shallow depth, failing in mineshafts with depths of hundreds of meters because of the limit of wireless communication and turbulence inside the mineshaft. In previous study, a three-dimensional (3D) scanning system with a single channel lidar was fabricated and operated using towed cable in a mineshaft to a depth of 200 m. The rotation and pendulum movement errors of the measuring unit were compensated for by applying the data of inertial measuring unit and comparing the similarity between the scan data of the adjacent depths (Kim et al., 2020). However, the errors grew with scan depth. In this paper, a multi-channel lidar sensor to obtain a continuous cross-sectional image of the mineshaft from a winch system pulled from bottom upward. In this new approach, within overlapped region viewed by the multi-channel lidar, rotation error was compensated for by comparing the similarity between the scan data at the same depth. The fabricated system was applied to scan 0-165 m depth of the mineshaft with 180 m depth. The reconstructed image was depicted in a 3D graph for interpretation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1374-1376
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• 2003

NASA, NSF and USGS jointly conducted a LIDAR survey over several sites in the Antarctic Dry Valleys and its vicinity, acquiring numerous surface points by NASA's Airborne Topographic Mapper (ATM) conical laser scanning altimetry system. The data set have high blunder ratio, and the conical scanning pattern resulted large variation of the point densities. Hence, to reduce the undesirable effects due to these characteristics and process the huge number of points with reasonable time and resources, we developed a novel approach to generate large-scale and high-resolution DEMs in robust, efficient and nearly automatic manners. Based on this approach we produced DEMs and then verified them with reference data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.137-143
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• 2002

In this study we generated digital orthophoto from LIDAR data. To generate digital orthophoto, we make TIN from raw laser scanning data(XYZ point data) and compiled DSM from this TIN. In this procedure much noise appeared along the break lines in DSM and this can give bad effect to the quality of digital orthophoto. Therefore, we applied various techniques which can refine the break line. In the result, we concluded that the fusion of LIDAR DEM of lowland and extracted buildings was adequate to generating DSM. So we generated the digital orthophoto from DSM generated from this technique. In the result of quality test, the positional accuracy of this digital orthophoto was better than the positional accuracy of 1:5,000 map.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1189-1196
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• 2023

Due to the acceleration of urbanization and advancements in technology, the importance of information related to indoor spaces has been increasing. Various scanning technologies are being developed to enable versatile utilization of the interior of buildings. In this paper, a system is proposed that utilizes 2D LIDAR for scanning, rotating, and moving LIDAR in the vertical direction to obtain a collection of 2D data, which is then aggregated to acquire 3D indoor spatial information. Finally, algorithms, including error correction, are applied to visualize the indoor structure in three dimensions and generate an output.