• 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.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.549-557
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• 2022

Tunnel face mapping involves the determination of rock discontinuities or weak rock conditions where extra support might be required. In this study, we investigated the application of Lidar scanning and photogrammetry to quantitatively characterize discontinuities of the rock mass on the tunnel face during excavation. The 3D models of tunnel faces generated by using these methods enable accurate and automatic discontinuity measurement to overcome the limitations of manual mapping. The results of this study show that both photogrammetry and Lidar can be used to reconstruct the 3D model of the tunnel face, although the photogrammetric 3D model is less detailed than its counterpart produced by Lidar. Given acceptable accuracy and cost-effectiveness, photogrammetry can be a fast, reliable, and low-cost alternative to Lidar for acquiring 3D models and determining rock discontinuities on tunnel faces.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.59-67
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• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.519-521
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• 2003

Within the paper an approach for the automatic extraction and reconstruction of buildings in urban built-up areas base on fusion of high-resolution satellite image and LIDAR data is presented. The presented data fusion scheme is essentially motivated by the fact that image and range data are quite complementary. Raised urban objects are first segmented from the terrain surface in the LIDAR data by making use of the spectral signature derived from satellite image, afterwards building potential regions are initially detected in a hierarchical scheme. A novel 3D building reconstruction model is also presented based on the assumption that most buildings can be approximately decomposed into polyhedral patches. With the constraints of presented building model, 3D edges are used to generate the hypothesis and follow the verification processes and a subsequent logical processing of the primitive geometric patches leads to 3D reconstruction of buildings with good details of shape. The approach is applied on the test sites and shows a good performance, an evaluation is described as well in the paper.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.150-157
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• 2015

Pedestrian detection has largely been researched as one of the important technologies for autonomous driving vehicle and preventing accidents. There are two categories for pedestrian detection, camera-based and LIDAR-based. LIDAR-based methods have the advantage of the wide angle of view and insensitivity of illuminance change while camera-based methods have not. However, there are several problems with 3D LIDAR, such as insufficient resolution to detect distant pedestrians and decrease in detection rate in a complex situation due to segmentation error and occlusion. In this paper, two methods using GM-PHD filter are proposed to improve the poor rates of pedestrian detection algorithms based on 3D LIDAR. First one improves detection performance and resolution of object by automatic accumulation of points in previous frames onto current objects. Second one additionally enhances the detection results by applying the GM-PHD filter which is modified in order to handle the poor situation to classified multi target. A quantitative evaluation with autonomously acquired road environment data shows the proposed methods highly increase the performance of existing pedestrian detection algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1061-1067
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• 2016

Position of the vehicle for driving is essential to autonomous navigation. However, there appears GPS position error due to multipath which is occurred by tall buildings in downtown area. In this paper, GPS position error is corrected by using camera sensor and highly accurate map made with 3D-Lidar. Input image through inverse perspective mapping is converted into top-view image, and it works out map matching with the map which has intensity of 3D-Lidar. Performance comparison was conducted between this method and traditional way which does map matching with input image after conversion of map to pinhole camera image. As a result, longitudinal error declined 49% and complexity declined 90%.

• 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.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.143-151
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• 2009

In this paper, a new extraction algorithm for artificial structure in 3D cloud points of terrestrial LIDAR is described, considering that various obstacles in terrestrial LIDAR make it difficult to apply conventional algorithms which are designed for air-born LIDAR data. Firstly we use the R, G, B color information from the terrestrial LIDAR data to discriminate among the massive 3D cloud points. Hough transform is then applied to estimate the straight lines that correspond to the target structure. Finally, the structure is extracted by comparing the distance between the estimated line and 3D cloud points. The proposed algorithm is efficient in the sense that it requires the user interaction only when the reference colors are obtained. Computer simulation shows the performance to be quite satisfactory.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.3
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• pp.233-240
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• 2013

Jakarta is the capital city of Indonesia which has problems of land subsidence with the rates of about 1 to 15 cm/year, up to 20-25 cm/year. The study has examined the land subsidence in Pantai Mutiara, Jakarta Bay which is a reclaimed area by using the Terrestrial LIDAR survey technique. The Terrestrial LIDAR survey results show that the survey site has mean elevation of 0.24 m with the highest elevation of 0.93 m and lowest - 0.35 m. Considering that AHHW (approximate highest high water) is 0.51 m, many areas of the survey site are lying below the AHHW. Pantai Mutiara area is showing various subsidence rates depending on sites although the site is relatively narrow and small (about 1 $km^2$). There is elevation differences of almost 1m within the site. In this study, key information including topography, dike height distribution, and future coastal flooding risk of the survey area was able to be provided by Terrestrial LIDAR survey conducted only once. Especially, as the 3D precision topography effectively conveys important messages relating to vulnerability of the site, policy makers and stakeholders can easily understand the situation of the site.