@inproceedings{01d3d62ff5c94a5b832d3513e46f6623,
title = "A ground segmentation method based on gradient fields for 3D point clouds",
abstract = "In order to navigate in an unknown environment, autonomous robots must distinguish traversable ground regions from impassible obstacles. Thus, ground segmentation is a crucial step for handling this issue. This study proposes a new ground segmentation method combining of two different techniques: gradient threshold segmentation and mean height evaluation. Ground regions near the center of the sensor are segmented using the gradient threshold technique, while sparse regions are segmented using mean height evaluation. The main contribution of this study is a new ground segmentation algorithm that can be applied to various 3D point clouds. The processing time is acceptable and allows real-time processing of sensor data.",
keywords = "3D point cloud, Gradient field, Segmentation",
author = "Hoang Vu and Nguyen, {Hieu Trong} and Phuong Chu and Seoungjae Cho and Kyungeun Cho",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; International Conference on Computer Science and its Applications, CSA 2017 ; Conference date: 18-12-2017 Through 20-12-2017",
year = "2018",
doi = "10.1007/978-981-10-7605-3_64",
language = "English",
isbn = "9789811076046",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "388--393",
editor = "Gangman Yi and Yunsick Sung and Park, {James J.} and Vincenzo Loia",
booktitle = "Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17",
address = "Germany",
}