Abstract
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’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.
Highlights
Since the advent of sufficient computational resources in a compact format, visual localisation and mapping have become driving technologies in many applications that require a mobile system to automatically map and localize within GPS-denied, previously unknown environments
The present paper explores the potential of passive multi-camera arrays, analyses the differences between the various camera array configurations, and presents a new algorithm for relative camera array pose estimation
In conclusion—by employing a camera matrix and leveraging trifocal tensor geometry—we present a novel, line-based relative pose solver that enables the first convincing, purely line-based visual odometry solution
Summary
Since the advent of sufficient computational resources in a compact format, visual localisation and mapping have become driving technologies in many applications that require a mobile system to automatically map and localize within GPS-denied, previously unknown environments. Our purely line-based 2D-2D relative pose solver consistently outperforms existing alternatives, and our camera-matrix visual odometry framework is able to compete with some of the most recent RGBD-SLAM solutions in terms of tracking accuracy. In conclusion—by employing a camera matrix and leveraging trifocal tensor geometry—we present a novel, line-based relative pose solver that enables the first convincing, purely line-based visual odometry solution. It rivals the state-of-the-art given by point-based methods for normal cameras, or even direct RGBD methods. RELATIVE POSE FOR CAMERA MATRICES WITH LINES As stated in [6], the trifocal tensor encapsulates all the (projective) geometric relations between three views that are independent of scene structure. The section concludes with the automatic generation of the actual pose solver
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