CASIA OpenIR  > 复杂系统认知与决策实验室
Robust and accurate depth estimation by fusing LiDAR and stereo
Xu, Guangyao1; Cao, Xuewei1; Liu, Jiaxin2; Fan, Junfeng1; Li, En1; Long, Xiaoyu1
发表期刊MEASUREMENT SCIENCE AND TECHNOLOGY
ISSN0957-0233
2023-12-01
卷号34期号:12页码:11
通讯作者Li, En(en.li@ia.ac.cn)
摘要Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the sensor's performance. Therefore, a precise and robust method for fusing LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo cameras, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo-matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.
关键词stereo matching LiDAR depth estimation multi-sensor fusion up-sampling
DOI10.1088/1361-6501/acef47
关键词[WOS]NETWORK
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[62273344] ; National Natural Science Foundation of China[61973300]
项目资助者National Natural Science Foundation of China
WOS研究方向Engineering ; Instruments & Instrumentation
WOS类目Engineering, Multidisciplinary ; Instruments & Instrumentation
WOS记录号WOS:001051857900001
出版者IOP Publishing Ltd
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/54012
专题复杂系统认知与决策实验室
中科院工业视觉智能装备工程实验室
通讯作者Li, En
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100000, Peoples R China
2.State Grid Liaoning Elect Power Co Ltd, Shenyang 110000, Peoples R China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Xu, Guangyao,Cao, Xuewei,Liu, Jiaxin,et al. Robust and accurate depth estimation by fusing LiDAR and stereo[J]. MEASUREMENT SCIENCE AND TECHNOLOGY,2023,34(12):11.
APA Xu, Guangyao,Cao, Xuewei,Liu, Jiaxin,Fan, Junfeng,Li, En,&Long, Xiaoyu.(2023).Robust and accurate depth estimation by fusing LiDAR and stereo.MEASUREMENT SCIENCE AND TECHNOLOGY,34(12),11.
MLA Xu, Guangyao,et al."Robust and accurate depth estimation by fusing LiDAR and stereo".MEASUREMENT SCIENCE AND TECHNOLOGY 34.12(2023):11.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Xu, Guangyao]的文章
[Cao, Xuewei]的文章
[Liu, Jiaxin]的文章
百度学术
百度学术中相似的文章
[Xu, Guangyao]的文章
[Cao, Xuewei]的文章
[Liu, Jiaxin]的文章
必应学术
必应学术中相似的文章
[Xu, Guangyao]的文章
[Cao, Xuewei]的文章
[Liu, Jiaxin]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。