• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.373-380
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• 2022

This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.

• Journal of Environmental Impact Assessment
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• v.17 no.5
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• pp.279-288
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• 2008

Vertical forest distribution is one of the important factors to understand various ecological mechanism such as succession, disturbance and environmental effects. LiDAR data provide information, both the horizontal and vertical distribution of forest structure. The laser scanner survey provided a point cloud, in which the x, y, and z coordinates of the points are known. The objectives of this study were 1) to analyze factors of forest structure such as individual tree isolation, tree height, canopy closure and tree density using LiDAR data and 2) to compare the forest structure between outer and interior forest. The paper conducted to extract the individual tree using watershed algorithm and to interpolate using the first return of LiDAR data for yielding digital surface model (DSM). The results of the study show characters of edge such as more isolated individual trees, higher density, lower canopy closure, and lower tree height than those of interior forest. LiDAR data is to be useful for analyzing of forest structure. Further study should be undertaken with species for more accurate results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.2
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• pp.11-17
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• 2009

Terrestrial LiDAR can measure high capacity 3D-topography coordinates and try to apply to various public works such as tunnel surveying, facility deformation surveying. This experiment is about how to calculate ground cutting volume because the stage of the earth work spend lots of money and time among civil engineering works. Surveying cutting area using Terrestrial LiDAR and then calculating cutting area in planned area comparing sectional plan before construction and planned section and LiDAR data. Also, the values of the calculating ground cutting volume by three different resolution LiDAR has are compared and analyzed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.191-199
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• 2023

In this paper, we propose a method to extract the features of five sensor-only facilities built as infrastructure for autonomous cooperative driving, which are from point cloud data acquired by LiDAR. In the case of image acquisition sensors installed in autonomous vehicles, the acquisition data is inconsistent due to the climatic environment and camera characteristics, so LiDAR sensor was applied to replace them. In addition, high-intensity reflectors were designed and attached to each facility to make it easier to distinguish it from other existing facilities with LiDAR. From the five sensor-only facilities developed and the point cloud data acquired by the data acquisition system, feature points were extracted based on the average reflective intensity of the high-intensity reflective paper attached to the facility, clustered by the DBSCAN method, and changed to two-dimensional coordinates by a projection method. The features of the facility at each distance consist of three-dimensional point coordinates, two-dimensional projected coordinates, and reflection intensity, and will be used as training data for a model for facility recognition to be developed in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.3-8
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• 2011

Recently, the demand for enterprise for service map providing and portal site services of a 3D virtual city model for public users has been expanding. Also, accuracy of the data, transfer rate and the update for the update for the lapse of time emerge are considered as more impertant factors, by providing 3D information with the web or mobile devices. With the latest technology, we have seen various 3D data through the web. With the VRML progressing actively, because it can provide a virtual display of the world and all aspects of interaction with web. It offers installation of simple plug-in without extra cost on the web. LiDAR system can obtain spatial data easily and accurately, as supprted by numerous researches and applications. However, in general, LiDAR data is obtained in the form of an irregular point cloud. So, in case of using data without converting, high processor is needed for presenting 2D forms from point data composed of 3D data and the data increase. This study expresses urban LiDAR data in 3D, 2D raster data that was applied by compressing algorithm that was used for solving the problems of large storage space and processing. For expressing 3D, algorithm that converts compressed LiDAR data into code Suited to VRML was made. Finally, urban area was expressed in 3D with expressing ground and feature separately.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.348-353
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• 2020

This paper presents an adaptive method to avoid obstacles in various environmental settings, using a two-dimensional (2D) LiDAR sensor for mobile robots. While the conventional reaction based smooth nearness diagram (SND) algorithms use a fixed safety distance criterion, the proposed algorithm autonomously changes the safety criterion considering the obstacle density around a robot. The fixed safety criterion for the whole SND obstacle avoidance process can induce inefficient motion controls in terms of the travel distance and action smoothness. We applied a multinomial logistic regression algorithm, softmax regression, to classify 2D LiDAR point clouds into seven obstacle structure classes. The trained model was used to recognize a current obstacle density situation using newly obtained 2D LiDAR data. Through the classification, the robot adaptively modifies the safety distance criterion according to the change in its environment. We experimentally verified that the motion controls generated by the proposed adaptive algorithm were smoother and more efficient compared to those of the conventional SND algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.1-9
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• 2020

ABL (Airborne Bathymetric LiDAR) is an advanced survey technology that uses green lasers to simultaneously measure the water depths and oceanic topography in coastal and river areas. Seabed point cloud extraction is an essential prerequisite to further utilizing the ABL data for various geographic data processing and applications. Conventional seabed detection approaches often use return waveforms. However, their limited accessibility often limits the broad use of the bathymetric LiDAR (Light Detection And Ranging) data. Further, it is often questioned if the waveform-based seabed extraction is reliable enough to extract seabed. Therefore, there is a high demand to extract seabed from the point cloud using other sources of information, such as geometric information. This study aimed to assess the feasibility of a ground filtering method to seabed extraction from geo-referenced point cloud data by using CSF (Cloth Simulation Filtering) method. We conducted a preliminary experiment with the RIGEL VQ 880 bathymetric data, and the results show that the CSF algorithm can be effectively applied to the seabed point segmentation.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.69-79
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• 2020

TRecently, smart cities that solve various problems in cities based on IoT technology are in the spotlight. In particular, cases of BIM application for smooth management of construction and maintenance are increasing, and spatial information is converted into 3D data through convergence technology and used for safety diagnosis. The purpose of this study is to create and combine point clouds of a multi-story building by using a ground laser scanner and a handheld LiDAR SLAM among UAV and LiDAR equipment, supplementing the Occluded area and disadvantages of each technology, examine the effectiveness of indoor and outdoor reverse design by observing shape reproduction and accuracy. As a result of the review, it was confirmed that the coordinate accuracy of the data was improved by creating and combining the indoor and outdoor point clouds of the multi-story building using three technologies. In particular, by supplementing the shortcomings of each technology, the completeness of the shape reproduction of the building was improved, the Occluded area and boundary were clearly distinguished, and the effectiveness of reverse engineering was verified.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.333-340
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• 2008

For the indoor spatial modeling by terrestrial LiDAR and the analyzing its positional accuracy result, two terrestrial LiDARs which have different specification each other were used at test site. This paper shows disparity of accuracy between (1) the structural coordinate transformation by point cloud unit using control points and (2) the relative registration among all point cloud units then structural coordinate transformation in bulk, under condition of limited number of control points. As results, the latter had smaller size and distribution of errors than the former although different specifications and acquistion methods are used.