DSOD: DSO in Dynamic Environments
文献类型：期刊论文
| 作者 | P.Ma; Y.Bai; J.N.Zhu; C.J.Wang; C.Peng |
| 刊名 | Ieee Access
 |
| 出版日期 | 2019 |
| 卷号 | 7页码:178300-178309 |
| 关键词 | DSO,dynamic environments,segmentation network,depth prediction,network,Computer Science,Engineering,Telecommunications |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/access.2019.2958374 |
| 英文摘要 | 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. |
| 语种 | 英语 |
| 源URL | [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. |
入库方式： OAI收割
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