DSOD: DSO in Dynamic Environments

doi:10.1109/access.2019.2958374

CIOMP OpenIR

	DSOD: DSO in Dynamic Environments
	P.Ma; Y.Bai; J.N.Zhu; C.J.Wang; C.Peng
	2019
发表期刊	Ieee Access
ISSN	2169-3536
卷号	7 页码:178300-178309
摘要	Recently, visual simultaneous localization and mapping (SLAM) has been widely used in robotics and autonomous vehicles. It performs well in static environments. However, real-world environments are often dynamic scenarios. Because it is difficult for SLAM to deal with moving objects such as pedestrians and moving cars, SLAM does not meet the actual needs of robots and autonomous vehicles in real-world scenarios. Visual odometry (VO) is a key component of SLAM systems. In this paper, to extend SLAM to dynamic scenarios, we propose a monocular VO based on direct sparse odometry (DSO) to solve the problems arising in a dynamic environment. The proposed method, called DSO-Dynamic (DSOD), combines a semantic segmentation network with a depth prediction network to provide prior depth and semantic information. Experiments were conducted on the KITTI and Cityscapes datasets, and the results show our method achieves good performance compared with the baseline algorithm, DSO.
关键词	DSO,dynamic environments,segmentation network,depth prediction,network,Computer Science,Engineering,Telecommunications
DOI	10.1109/access.2019.2958374
收录类别	SCI ; EI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/63144
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	P.Ma,Y.Bai,J.N.Zhu,et al. DSOD: DSO in Dynamic Environments[J]. Ieee Access,2019,7:178300-178309.
APA	P.Ma,Y.Bai,J.N.Zhu,C.J.Wang,&C.Peng.(2019).DSOD: DSO in Dynamic Environments.Ieee Access,7,178300-178309.
MLA	P.Ma,et al."DSOD: DSO in Dynamic Environments".Ieee Access 7(2019):178300-178309.

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DSOD DSO in Dynamic （2018KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载