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공지사항
논문명 |
Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems |
논문종류 |
SCI |
저자 |
Phuong Minh Chu, Seoungjae Cho, Jisun Park1, Simon Fong and Kyungeun Cho |
Impact Factor |
3.7 |
게재학술지명 |
Human-centric Computing and Information Sciences |
게재일 |
2019.05 |
Ground segmentation is an important step for any autonomous and remote-controlled systems. After separating ground and nonground parts, many works such as
object tracking and 3D reconstruction can be performed. In this paper, we propose
an efcient method for segmenting the ground data of point clouds acquired from
multi-channel Lidar sensors. The goal of this study is to completely separate ground
points and nonground points in real time. The proposed method segments ground
data efciently and accurately in various environments such as fat terrain, undulating/
rugged terrain, and mountainous terrain. First, the point cloud in each obtained frame
is divided into small groups. We then focus on the vertical and horizontal directions
separately, before processing both directions concurrently. Experiments were conducted, and the results showed the efectiveness of the proposed ground segment
method. For fat and sloping terrains, the accuracy is over than 90%. Besides, the quality
of the proposed method is also over than 80% for bumpy terrains. On the other hand,
the speed is 145 frames per second. Therefore, in both simple and complex terrains, we
gained good results and real-time performance |
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