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Indexed by:会议论文

Date of Publication:2016-07-27

Included Journals:EI、CPCI-S、Scopus

Volume:2016-August

Page Number:7131-7136

Key Words:Fast Scene Segmentation; 3D Point Clouds Classification; Conditional Random Field; Urban Environments

Abstract:3D scene segmentation and classification are two challenging tasks for unmanned ground vehicles (UGVs) working in urban environments. According to the structured characters existing in urban scenes, a novel 3D scene segmentation approach is proposed based on region growing algorithm using sequential 2D laser scanning data. Since no iteration procedure is used in this approach, it can perform fast segmentation than traditional ones such as k-means based segmentation approach. A Conditional Random Field (CRF) model is utilized in our urban scene classification framework, in which each segmented patch is selected as a CRF node and the high-order cliques are generated by a clustering algorithm. The experimental results based on DUT2 dataset and KAIST dataset are given to demonstrate the validity and robustness of the proposed approach.