Position Estimation of Camera Based on Unsupervised Learning
This work addresses camera localization for applications like robotics and augmented reality, but it is incremental as it builds directly on a prior method.
The paper tackles camera pose estimation from video sequences by improving an existing unsupervised learning method for monocular depth recovery and pose estimation, introducing more reasonable inter-frame constraints and synthesizing camera trajectories in a unified world coordinate system, resulting in better performance.
It is an exciting task to recover the scene's 3d-structure and camera pose from the video sequence. Most of the current solutions divide it into two parts, monocular depth recovery and camera pose estimation. The monocular depth recovery is often studied as an independent part, and a better depth estimation is used to solve the pose. While camera pose is still estimated by traditional SLAM (Simultaneous Localization And Mapping) methods in most cases. The use of unsupervised method for monocular depth recovery and pose estimation has benefited from the study of [1] and achieved good results. In this paper, we improve the method of [1]. Our emphasis is laid on the improvement of the idea and related theory, introducing a more reasonable inter frame constraints and finally synthesize the camera trajectory with inter frame pose estimation in the unified world coordinate system. And our results get better performance.