Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera
This addresses a specific challenge in computer vision and robotics for systems using multi-modal stereo cameras, but it is incremental as it builds on existing scale estimation methods for monocular SfM.
The paper tackles the problem of estimating absolute scale for monocular structure-from-motion (SfM) in a multi-modal stereo camera system with mismatched spectral images (e.g., RGB and FIR), where direct feature matching is difficult due to low resolution and texture. It proposes a method using batch optimization based on epipolar constraints with a small number of correspondences, achieving verified accuracy and stability in synthetic and real experiments.
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.