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[SCI]Transforming a 3-D LiDAR Point Cloud Into a 2-D Dense Depth Map Through a Parameter Self-Adaptive Framework

时间:2017-04-01 来源:未知 点击:
Transforming a 3-D LiDAR Point Cloud Into a 2-D Dense Depth Map Through a Parameter Self-Adaptive Framework

作者:Chen, L (Chen, Long)1 ] He, YH (He, Yuhang)2,3 ] Chen, JD (Chen, Jianda)1 ] Li, QQ (Li, Qingquan)4 ] Zou, Q (Zou, Qin)5 ]

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

 

卷: 18

 

期: 1

 

页: 165-176

DOI: 10.1109/TITS.2016.2564640

出版年: JAN 2017


摘要

The 3-D LiDAR scanner and the 2-D chargecoupled device (CCD) camera are two typical types of sensors for surrounding-environment perceiving in robotics or autonomous driving. Commonly, they are jointly used to improve perception accuracy by simultaneously recording the distances of surrounding objects, as well as the color and shape information. In this paper, we use the correspondence between a 3-D LiDAR scanner and a CCD camera to rearrange the captured LiDAR point cloud into a dense depth map, in which each 3-D point corresponds to a pixel at the same location in the RGB image. In this paper, we assume that the LiDAR scanner and the CCD camera are accurately calibrated and synchronized beforehand so that each 3-D LiDAR point cloud is aligned with its corresponding RGB image. Each frame of the LiDAR point cloud is then projected onto the RGB image plane to form a sparse depth map. Then, a self-adaptive method is proposed to upsample the sparse depth map into a dense depth map, in which the RGB image and the anisotropic diffusion tensor are exploited to guide upsampling by reinforcing the RGB-depth compactness. Finally, convex optimization is applied on the dense depth map for global enhancement. Experiments on the KITTI and Middlebury data sets demonstrate that the proposed method outperforms several other relevant state-of-the-art methods in terms of visual comparison and root-mean- square error measurement.

基金资助致谢
基金资助机构 授权号
National Natural Science Foundation of China 
41401525 
61301277 
41371431 
Guangdong Provincial Natural Science 
2014A030313209 
CCF-Tencent Open Fund 
tIAGR20150114 
出版商

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA

类别 / 分类

研究方向:Engineering; Transportation

Web of Science 类别:Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology

文献信息

文献类型:Article

语种:English

入藏号: WOS:000396139200014

ISSN: 1524-9050

eISSN: 1558-0016

期刊信息

Impact Factor (影响因子): 2.534