Egomotion Estimation Using Assorted Features

Abstract

We propose a novel minimal solver for recovering camera motion across two views of a calibrated stereo rig. The algorithm can handle any assorted combination of point and line features across the four images and facilitates a visual odometry pipeline that is enhanced by well-localized and reliably-tracked line features while retaining the well-known advantages of point features. The mathematical framework of our method is based on trifocal tensor geometry and a quaternion representation of rotation matrices. A simple polynomial system is developed from which camera motion parameters may be extracted more robustly in the presence of severe noise, as compared to the conventionally employed direct linear/subspace solutions. This is demonstrated with extensive experiments and comparisons against the 3-point and line-sfm algorithms.