Research Repository

A novel outdoor scene-understanding framework for unmanned ground vehicles with 3D laser scanners

Zhuang, Yan and He, Guojian and Hu, Huosheng and Wu, Zhenwei (2015) 'A novel outdoor scene-understanding framework for unmanned ground vehicles with 3D laser scanners.' Transactions of the Institute of Measurement and Control, 37 (4). pp. 435-445. ISSN 0142-3312

Full text not available from this repository.


<jats:p> Outdoor scene understanding plays a key role for unmanned ground vehicles (UGVs) to navigate in complex urban environments. This paper presents a novel 3D scene-understanding framework for UGVs to handle uncertain and changing lighting conditions outdoors. A 2D bearing angle (BA) image is deployed to perform scene understanding so that the computational burden in the process of segmentation and classification of the 3D laser point cloud can be reduced. An improved super-pixel algorithm is used for fast 3D scene segmentation, and then the Gentle–Adaboost algorithm is utilized to perform super-pixel patch classification using the texture features of the Gray Level Co-occurrence Matrix. All false classification results in the uncertain super-pixel patches of BA images are transformed back to raw 3D laser points and a re-classification is conducted to refine the 3D scene understanding for UGVs. The results from a real laser dataset taken from a large-scale campus environment show the validity and robust performance of the proposed approach, in comparison with the results from Korea Advanced Institute of Science and Technology dataset. </jats:p>

Item Type: Article
Uncontrolled Keywords: 3D point clouds; laser scanner; unmanned ground vehicles; urban scene understanding
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 16 Apr 2015 11:13
Last Modified: 15 Jan 2022 00:41

Actions (login required)

View Item View Item