A Pose-only Solution to Visual Reconstruction and Navigation

TL;DR

This paper introduces a pose-only imaging geometry framework that enables efficient, real-time 3D scene reconstruction and navigation by simplifying camera motion estimation and eliminating nonlinear optimization.

Contribution

The authors propose a linear, pose-only approach for visual reconstruction that significantly improves computational efficiency and robustness over traditional methods.

Findings

Scene and camera pose recovery is 2-4 orders of magnitude faster.

The method avoids nonlinear optimization, enabling real-time applications.

Experiments validate the efficiency and robustness of the proposed framework.

Abstract

Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenes and critical camera motions are great challenges facing the research community to achieve this goal. We raised a pose-only imaging geometry framework and algorithms that can help solve these challenges. The representation is a linear function of camera global translations, which allows for efficient and robust camera motion estimation. As a result, the spatial feature coordinates can be analytically reconstructed and do not require nonlinear optimization. Experiments demonstrate that the computational efficiency of recovering the scene and associated camera poses is significantly improved by 2-4 orders of magnitude. This solution might be promising to unlock real-time 3D visual computing in many forefront applications.