Linear Global Translation Estimation with Feature Tracks

TL;DR

This paper introduces a robust, efficient linear method for global camera translation estimation using feature tracks, capable of handling collinear motion and weak associations without scene point coordinates.

Contribution

A novel linear position constraint for cameras that improves robustness and efficiency in translation estimation, even with challenging motion and data conditions.

Findings

Effective on sequential and unordered images

Robust to outliers in essential matrices and feature matches

Demonstrates high accuracy and efficiency

Abstract

This paper derives a novel linear position constraint for cameras seeing a common scene point, which leads to a direct linear method for global camera translation estimation. Unlike previous solutions, this method deals with collinear camera motion and weak image association at the same time. The final linear formulation does not involve the coordinates of scene points, which makes it efficient even for large scale data. We solve the linear equation based on $L_1$ norm, which makes our system more robust to outliers in essential matrices and feature correspondences. We experiment this method on both sequentially captured images and unordered Internet images. The experiments demonstrate its strength in robustness, accuracy, and efficiency.