Egomotion Using Assorted Features

Abstract

We describe a novel and robust minimal solver for performing online visual odometry with a stereo rig. The proposed method can compute the underlying camera motion given any arbitrary, mixed combination of point and line correspondences across two stereo views. This facilitates a hybrid visual odometry pipeline that is enhanced by well-localized and reliably-tracked line features while retaining the well-known advantages of point features. Utilizing trifocal tensor geometry and quaternion representation of rotation matrices, we develop a polynomial system from which camera motion parameters can be robustly extracted in the presence of noise. We show how the more popular approach of using direct linear/subspace techniques fail in this regard and demonstrate improved performance using our formulation with extensive experiments and comparisons against the 3-point and line-sfm algorithms.