International Journal of Computer Vision

, Volume 98, Issue 2, pp 202–216

Egomotion Estimation Using Assorted Features

Article

DOI: 10.1007/s11263-011-0504-5

Cite this article as:
Pradeep, V. & Lim, J. Int J Comput Vis (2012) 98: 202. doi:10.1007/s11263-011-0504-5

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.

Keywords

Visual odometry SLAM Structure from motion Tracking 

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Applied Sciences GroupMicrosoft CorporationRedmondUSA
  2. 2.Honda Research InstituteMountain ViewUSA