• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.73-78
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• 2009

Real-time 3D visualization of terrain data is one of the important issues in GIS(Geographic Information System) field. We present a real-time terrain rendering engine that can use several types of GIS data source such as DEM(Digital Elevation Map), DTED(Digital Terrain Elevation Data) and LIDAR(Light Detection And Ranging). Our rendering engine is a quadtree-based terrain rendering framework with several acceleration modules. This can generate an ocular and binocular image. Also it can be applied to the flight simulation, walk-through simulation and a variety of GIS applications.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.79-82
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• 2005

Recently the necessity of road data is still being increased in industrial society, so there are many repairing and new constructions of roads at many areas. According to the development of government, city and region, the update and acquisition of road data for GIS (Geographical Information System) is very necessary. In this study, the fusion method with range data(3D Ground Coordinate System Data) and Intensity data in stand alone LiDAR data is used for road extraction and then digital image processing method is applicable. Up to date Intensity data of LiDAR is being studied. This study shows the possibility method for road extraction using Intensity data. Intensity and Range data are acquired at the same time. Therefore LiDAR does not have problems of multi-sensor data fusion method. Also the advantage of intensity data is already geocoded, same scale of real world and can make ortho-photo. Lastly, analysis of quantitative and quality is showed with extracted road image which compare with I: 1,000 digital map.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.177-180
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• 2008

Many researches have been tried to extract building models and created a 3D cyber city from LiDAR data. In this paper, the approach of extracting complex building by using airborne LiDAR data combined with airborne orthoimagery has been performed. The pseudo-building elevations were derived from modified discrete return LiDAR data. Based on information property of the pseudo-height, building features could be extracted. The results of this study indicated the improvement of building extraction.

• Annual Conference of KIPS
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• 2016.10a
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• pp.681-682
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• 2016

In this study we propose a novel approach to segment a terrain in two parts: ground and none-ground. The terrain is gained by a multi-channel 3D laser range sensor. We process each vertical line in each frame data. The vertical line is bounded by the sensor's position and a point in the largest circle of the frame. We consider each pair of two consecutive points in each line to find begin-ground and end-ground points. All points placed between a begin-ground point and an end-ground point are ground ones. The other points are none-ground. After examining all vertical lines in the frame, we obtain the terrain segmentation result.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.269-279
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• 2020

This paper presents the design and implementation of embedded systems and autonomous path generation for autonomous speed sprayer. Autonomous Orchard Systems can be divided into embedded controller and path generation module. Embedded controller receives analog sensor data, on/off switch data and control linear actuator, break, clutch and steering module. In path generation part, we get 3D cloud point using Velodyne VLP16 LIDAR sensor and process the point cloud to generate maps, do localization, generate driving path. Then, it finally generates velocity and rotation angle in real time, and sends the data to embedded controller. Embedded controller controls steering wheel based on the received data. The developed autonomous speed sprayer is verified in test-bed with apple tree-shaped artworks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.545-555
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• 2007

3D buildings are the most prominent feature comprising urban scene. A few of mega-cities in the globe are virtually reconstructed in photo-realistic 3D models, which becomes accessible by the public through the state-of-the-art online mapping services. A lot of research efforts have been made to develop automatic reconstruction technique of large-scale 3D building models from remotely sensed data. However, existing methods still produce irregular building polygons due to errors induced partly by uncalibrated sensor system, scene complexity and partly inappropriate sensor resolution to observed object scales. Thus, a geometric regularization technique is urgently required to rectify such irregular building polygons that are quickly captured from low sensory data. This paper aims to develop a new method for regularizing noise building outlines extracted from airborne LiDAR data, and to evaluate its performance in comparison with existing methods. These include Douglas-Peucker's polyline simplication, total least-squared adjustment, model hypothesis-verification, and rule-based rectification. Based on Minimum Description Length (MDL) principal, a new objective function, Geometric Minimum Description Length (GMDL), to regularize geometric noises is introduced to enhance the repetition of identical line directionality, regular angle transition and to minimize the number of vertices used. After generating hypothetical regularized models, a global optimum of the geometric regularity is achieved by verifying the entire solution space. A comparative evaluation of the proposed geometric regulator is conducted using both simulated and real building vectors with various levels of noise. The results show that the GMDL outperforms the selected existing algorithms at the most of noise levels.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.204-211
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• 2021

In this study, information on vegetation was collected using a point cloud through a 3-D Terrestrial Lidar Scanner, and the physical shape was analyzed by reconfiguring the object based on the refined data. Each filtering step of the raw data was optimized, and the reference volume and the estimated results using the Alpha Shape and Voxel techniques were compared. As a result of the analysis, when the volume was calculated by applying the Alpha Shape, it was overestimated than reference volume regardless of data filtering. In addition, the Voxel method to be the most similar to the reference volume after the 8th filtering, and as the filtering proceeded, it was underestimated. Therefore, when re-implementing an object using a point cloud, internal voids due to the complex shape of the target object must be considered, and it is necessary to pay attention to the filtering process for optimal data analyzed in the filtering process.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.92-100
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• 2012

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.342-348
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• 2016

We propose an obstacle detection algorithm for unmanned ground vehicle on uneven terrain. The key ideas of the proposed algorithm are the use of two-layer laser range data to calculate the gradient of a target, which is characterized as either ground or obstacles. The proposed obstacle detection algorithm includes 4-steps: 1) Obtain the distance data for each angle from multiple lidars or a multi-layer scan lidar. 2) Calcualate the gradient for each angle of the uneven terrain. 3) Determine ground or obstacle for each angle on the basis of reference gradient. 4) Generate a new distance data for each angle for a virtual laser scanner. The proposed algorithm is verified by various experiments.