Title: A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines,
Abstract: We propose a minimal solution for pose estimation using both points and lines
for a multi-perspective camera. In this paper, we treat the multi-perspective
camera as a collection of rigidly attached perspective cameras. These type of
imaging devices are useful for several computer vision applications that
require a large coverage such as surveillance, self-driving cars, and
motion-capture studios. While prior methods have considered the cases using
solely points or lines, the hybrid case involving both points and lines has not
been solved for multi-perspective cameras. We present the solutions for two
cases. In the first case, we are given 2D to 3D correspondences for two points
and one line. In the later case, we are given 2D to 3D correspondences for one
point and two lines. We show that the solution for the case of two points and
one line can be formulated as a fourth degree equation. This is interesting
because we can get a closed-form solution and thereby achieve high
computational efficiency. The later case involving two lines and one point can
be mapped to an eighth degree equation. We show simulations and real
experiments to demonstrate the advantages and benefits over existing methods.