Robust Dense Visual Odometry with boundary pixel suppression
文献类型：会议论文
| 作者 | He YJ(贺一家) ; Guo Yue(郭跃); Ye Aixue(叶爱学); Wen Feng(温丰); Yuan Kui(原魁)
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| 出版日期 | 2017-03 |
| 会议日期 | 2016-12 |
| 会议地点 | 青岛 |
| 关键词 | Visual Odometry Rgbd |
| 英文摘要 | Pose estimation and 3D environment reconstruction are crucial for autonomous navigation in mobile robotics. Robust dense visual odometry based on a RGB-D sensor uses all pixels to estimate frame-to-frame motion by minimizing the photometric and geometric error. 3D coordinates of each pixel are calculated necessarily with its corresponding depth. However, depths of some pixels near object boundaries from RGB-D sensors are not accurate. The general robust dense visual odometry does not consider depth noise impact for photometric error and geometric error. In this paper, we construct uncertainties of photometric error and geometric error with depth noise and point out depth noise near object boundaries can significantly affect the result of motion estimation. We present a modified robust dense visual odometry with boundary pixel suppression. Publicly available benchmark datasets are employed to evaluate our system, and results showed that our method achieves higher accuracy compared with the state-of-the-art Dense Visual Odometry (DVO). |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21065]  |
| 专题 | 自动化研究所_智能制造技术与系统研究中心_智能机器人团队 |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | He YJ,Guo Yue,Ye Aixue,et al. Robust Dense Visual Odometry with boundary pixel suppression[C]. 见:. 青岛. 2016-12. |
入库方式： OAI收割
来源：自动化研究所
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