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Robust Dense Visual Odometry with boundary pixel suppression
He YJ(贺一家); Guo Yue(郭跃); Ye Aixue(叶爱学); Wen Feng(温丰); Yuan Kui(原魁)
Conference Name2016 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Conference Date2016-12
Conference Place青岛
AbstractPose 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).
KeywordVisual Odometry Rgbd
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
He YJ,Guo Yue,Ye Aixue,et al. Robust Dense Visual Odometry with boundary pixel suppression[C],2017.
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