• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.555-563
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• 2009

When a mobile mapping system or a robot is equipped with only a GPS (Global Positioning System) and multiple stereo camera system, a transformation from a local camera coordinate system to GPS coordinate system is required to link camera poses and 3D data by V-SLAM (Vision based Simultaneous Localization And Mapping) to GIS data or remove the accumulation error of those camera poses. In order to satisfy the requirements, this paper proposed a novel method that calculates a camera rotation in the GPS coordinate system using the three pairs of camera positions by GPS and V-SLAM, respectively. The propose method is composed of four simple steps; 1) calculate a quaternion for two plane's normal vectors based on each three camera positions to be parallel, 2) transfer the three camera positions by V-SLAM with the calculated quaternion 3) calculate an additional quaternion for mapping the second or third point among the transferred positions to a camera position by GPS, and 4) determine a final quaternion by multiplying the two quaternions. The final quaternion can directly transfer from a local camera coordinate system to the GPS coordinate system. Additionally, an update of the 3D data of captured objects based on view angles from the object to cameras is proposed. This paper demonstrated the proposed method through a simulation and an experiment.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.10
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• pp.49-54
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• 2007

As digital contents based on 3D graphics are increased, the requirement for low power 3D graphic hardware for mobile devices is increased. We design a transformation engine for mobile 3D graphic processor. We propose a simplified transformation engine for mobile 3D graphic processor. The area of the transformation engine is reduced by merging a mapping transformation unit into a projective transformation unit and by replacing a clipping unit with a selection unit. It consists of a viewing transformation unit a projective transformation unit a divide by w nit, and a selection unit. It can process 32 bit floating point format of the IEEE-754 standard or a reduced 24 bit floating point format. It has a pipelined architecture so that a vertex is processed every 4 cycles except for the initial latency. The RTL code is verified using an FPGA.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.539-547
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• 2012

Volume survey for earthwork is conducted periodically at every month at large scaled construction sites for request payment for the construction. Currently, the earthwork volume is calculated on the overlapped drawing for two cross sections which were created by new and previous surveying data observed by GPS and TS. However the survey is required lots of time and expenses as it is a pin-point surveying method thus surveyors intend to get data only for the heavy undulated topographic features chosen by himself to save time and expenses. Consequently, it causes poor results due to low dense measurement as well as it's not matched with the results by another surveyors. As a result, the conflicts related in earthwork volume calculation often arises among the owner, contractors and sub contractors. In this study, we developed a method to get surveying data for fast and accurate volume calculation using 3D Mobile Laser System and conducted an application test. Also we carried out experiments for topographic survey using MLS to judge for the application additionally. The results showed that we could reduce a time for cross section survey from 48 hours to 2 hours and collected high dense data which have 0.2m interval instead of 3m interval compare with existing method. Also we could make a map which has high accuracy within 10cm in horizontal through topographic survey using MLS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of Navigation and Port Research
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• v.40 no.6
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• pp.369-374
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• 2016

Recently, due to the rapid development of smart computing technology, there have been a lot of studies on 3D navigation, and consequently the advanced commercial 3D navigation system and 3D VTS system are on the market. Even the 3D technology is well known and wide spreaded, unfortunately, the secret of code behind is still unleashed. The purpose of this paper is to show the basic methodologies to display ENC features under 3D display environment to meet the requirement of S-52 standards. The OpenGL ES is used to develop 3D ENC Viewer for further use on mobile platforms and web based vessel monitoring system. P articularly, area object triangulation, complex line drawing, polygon pattern filling and symbol drawing by texture mapping are explained in detail. The result of performance test is shown as table for correlation of SENC file size to display speed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.243-251
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• 2019

The road sign location information installed on national roads is continuously updated using MMS (Mobile Mapping System) technology. It is possible to map accurate road facilities by MMS, but the equipment is very expensive and requires specialized technology. Also, the accuracy of the position of the object greatly depends on the GPS (Global Positioning System) accuracy. In the case of road facility mapping, the advantage of drone is more remarkable than that of field survey or conventional aerial photogrammetry. In particular, it is more efficient than field surveying and it is possible to acquire high resolution images with low budget compared to conventional aerial photogrammetry. In this study, the accuracy of the location information measured by the existing MMS is compared with the GPS survey result and the accuracy analysis is performed by the drone aerial photogrammetry. In order to confirm the space accuracy that can be obtained when conducting drone aerial photogrammetry, the accuracy of the change in the number of ground control points and the degree of overlap was evaluated. As a result of the experiment, it was possible to obtain sufficient accuracy with two ground control points distributed at both ends of the road and 60% overlap.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.221-224
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• 2007

MMS(Mobile Mapping System) generates a efficient image data for mapping and facility management. However, this image data of MMS has many difficulties in a practical use because of huge data volume. Therefore the important information likes road sign post must be extracted from huge MMS image data. In Korea, there is the HMS(Highway Management System) to manage a national road that acquire the line and condition of road from the MMS images. In the HMS each road sign information is manually inputted by the keyboard from moving MMS. This manually passive input way generate the error like inaccurate position, mistaking input in this research we developed the automatic road sign identifying technique using the image processing and the direct geo-referencing by GPS/INS data. This development brings not only good flexibility for field operations, also efficient data processing in HMS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2023-2030
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• 2010

In this paper, we present research results of developing high performance scan conversion unit and implementing it on FPGA chip. To increase performance of scan conversion unit, we propose an architecture of scan converter that is a SIMD architecture and uses tile-based traversing method. The proposed scan conversion unit can operate about 124Mhz clock frequency on Xilinx Vertex4 LX100 device. To verify the scan conversion unit, we also develop shader unit, texture mapping unit and $240{\times}320$ color TFT-LCD controller to display outputs of the scan conversion unit on TFT-LCD. Because the scan conversion unit implemented on FPGA has 311Mpixels/sec pixel rate, it is applicable to desktop pc's 3d graphics system as well as mobile 3d graphics system needing high pixel rates.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.286-291
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• 2020

In this paper, we propose a system that recommends music through tempo-oriented music classification and sensor-based human activity recognition. The proposed method indexes music files using tempo-oriented music classification and recommends suitable music according to the recognized user's activity. For accurate music classification, a dynamic classification based on a modulation spectrum and a sequence classification based on a Mel-spectrogram are used in combination. In addition, simple accelerometer and gyroscope sensor data of the smartphone are applied to deep spiking neural networks to improve activity recognition performance. Finally, music recommendation is performed through a mapping table considering the relationship between the recognized activity and the indexed music file. The experimental results show that the proposed system is suitable for use in any practical mobile device with a music player.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.