Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera

TL;DR

This paper introduces a new method for estimating the absolute scale in monocular structure-from-motion systems using multi-modal stereo cameras with different spectral images, addressing challenges in feature matching.

Contribution

It presents a novel batch optimization approach leveraging epipolar constraints to estimate scale from limited feature correspondences between RGB and FIR images.

Findings

Method achieves accurate scale estimation in synthetic experiments.

Method demonstrates robustness with limited feature matches in real images.

Proposed approach improves monocular SfM in multi-modal stereo setups.

Abstract

This paper proposes a novel method of estimating the absolute scale of monocular SfM for a multi-modal stereo camera. In the fields of computer vision and robotics, scale estimation for monocular SfM has been widely investigated in order to simplify systems. This paper addresses the scale estimation problem for a stereo camera system in which two cameras capture different spectral images (e.g., RGB and FIR), whose feature points are difficult to directly match using descriptors. Furthermore, the number of matching points between FIR images can be comparatively small, owing to the low resolution and lack of thermal scene texture. To cope with these difficulties, the proposed method estimates the scale parameter using batch optimization, based on the epipolar constraint of a small number of feature correspondences between the invisible light images. The accuracy and numerical stability of the proposed method are verified by synthetic and real image experiments.