Camera pose estimation based on PnL with a known vertical direction

Abstract

In this paper, we address the problem of camera pose estimation using 2D and 3D line features, also known as PnL (Perspective-n-Line) with a known vertical direction. The minimal number of line correspondences required to estimate the complete camera pose is 3 (P3L) in the general case, yielding to a minimum of 8 possible solutions. Prior knowledge of the vertical direction, such as provided by common sensors (e.g. Inertial Measurement Unit, or IMU), reduces the problem to a 4 Degree of Freedom (DoF) problem and outputs a single solution. We benefit this fact to decouple the remaining rotation estimation and the translation estimation and we present a two-fold contribution: (1) we present a linear formulation of the PnL problem in Plucker lines coordinates with a known ¨ vertical direction, including a Gauss-Newton-based orientation and location refinement to compensate IMU sensor noise. (2) we propose a new efficient RANdom SAmple Consensus (RANSAC) scheme for both feature pairing and outliers rejection based solely on rotation estimation from 2 line pairs. This greatly diminishes the computational cost compared to a RANSAC3 or RANSAC4 scheme. We evaluate our algorithms on synthetic data and on our own real dataset. Experimental results show state of the art results in term of accuracy and runtime, when facing 2D noise, 3D noise and vertical direction sensor noise