• Journal of Information Processing Systems
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• 제13권3호
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• pp.491-499
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• 2017

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2005년도 추계학술대회
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• pp.81-104
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• 2005

가상공간 시현이나 GIS 및 이동통신과 같은 다양한 응용분야에 정확한 3차원 도시모델은 기본적인 자료가 되고 있다. LiDAR 시스템은 대상물의 3차원 정보를 직접적으로 획득할 수 있는 새로운 시스템이다. LiDAR 자료로부터 수동적으로 3차원 정보를 구축하는 것은 많은 시간과 비용을 필요로 한다. 이와 같은 이유로 많은 연구가 자동화에 그 초점을 맞추고 있다. 본 연구에서는 필터링기법을 이용해서 LiDAR 자료로부터 지면과 비지면을 분류하고, 지면점을 이용하여 DTM을 생성하고, 비지면점을 이용해서 건물을 구축하였다. 정확도의 평가결과 DTM은 고층아파트지역에서 약 0.16m, 저층주거지역에서 0.59m의 오류가 나타났으며, 건물의 경우 1/5,000 수치지형도의 정밀도와 부합하는 결과를 나타냈다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 추계학술발표대회
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• pp.733-734
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• 2016

This study proposes a novel approach for ground segmentation of 3D point cloud. We combine two techniques: gradient threshold segmentation, and mean height evaluation. Acquired 3D point cloud is represented as a graph data structures by exploiting the structure of 2D reference image. The ground parts nearing the position of the sensor are segmented based on gradient threshold technique. For sparse regions, we separate the ground and nonground by using a technique called mean height evaluation. The main contribution of this study is a new ground segmentation algorithm which works well with 3D point clouds from various environments. The processing time is acceptable and it allows the algorithm running in real time.