Efficient Pose Tracking based on Line Segment Matching

Abstract

Pose tracking is a crucial issue for many applications such as robotic tasks and facility operations. Vision-based approaches with non-contact properties are appropriate choices for these tasks. However, vision-based approaches are not sufficiently robust and fast. In this work, we propose a vision-based pose tracking to deal with these problems. We estimate poses using Lie group and Lie algebra representation theory. Such operation is performed in a linearized space, therefore it is convenient for pose estimation. To provide reliable visual information for our pose estimation, we detect line segments. Our detection of line segment depends on semi-global image information. We describe all line segments and match those detected in consecutive frames. Our line segment detector and matching descriptor are good at discarding ambiguous line segments and finding real ones in noisy situations. The integration of group theory and line segment detection and matching plays an important role for developing a robust vision-based pose tracking system. Our system proves to be efficient and robust.