Position Estimation of Camera Based on Unsupervised Learning

TL;DR

This paper improves unsupervised learning methods for monocular depth and camera pose estimation from video sequences, integrating inter-frame constraints and synthesizing camera trajectories for enhanced accuracy.

Contribution

It introduces a more reasonable inter-frame constraint approach and unifies camera trajectory synthesis in a single coordinate system, improving upon previous methods.

Findings

Enhanced camera pose estimation accuracy

Better monocular depth recovery results

Unified trajectory synthesis in world coordinates

Abstract

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.