Accurate Line-Based Relative Pose Estimation With Camera Matrices

doi:10.1109/ACCESS.2020.2992505

Accurate Line-Based Relative Pose Estimation With Camera Matrices

2020-01-01

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DOI：10.1109/ACCESS.2020.2992505

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While most monocular structure-from-motion frameworks rely on sparse keypoints, it has long been acknowledged that lines represent an alternative, higher-order feature with high accuracy, repeatability, and abundant availability in man-made environments. Its exclusive use, however, is severely complicated by its inability to resolve the common bootstrapping scenario of two-view geometry. Even with stereo cameras, a one-dimensional disparity space, as well as ill-posed triangulations of horizontal lines make the realization of purely line-based tracking pipelines difficult. The present paper successfully leverages the redundancy in camera matrices to alleviate this shortcoming. We present a novel stereo trifocal tensor solver and extend it to the case of two camera matrix view-points. Our experiments demonstrate superior behavior with respect to both 2D-2D and 3D-3D alternatives. We furthermore outline the camera matrix & x2019;s ability to continuously and robustly bootstrap visual motion estimation pipelines via integration into a robust, purely line-based visual odometry pipeline. The result leads to state-of-the-art tracking accuracy comparable to what is achieved by point-based stereo or even dense depth camera alternatives.

Keywords:

Light-field cameras

multi-camera arrays

plenoptic vision

line features

trifocal tensor

automatic solver generator

Grobner basis

Accurate Line-Based Relative Pose Estimation With Camera Matrices

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