Image Uncertainty-Based Absolute Camera Pose Estimation with Fibonacci Outlier Elimination

Abstract

Estimating the pose of a camera is a vital requirement in real-world applications like virtual reality, structure from motion, vision-assisted robot localization and manipulation. The existing Perspective-n-Point (PnP) based pose estimation algorithms have poor accuracy in presence of noise and outliers. Hence, they are combined with the Random Sample Consensus strategies to eliminate the outliers prior to pose estimation and to produce accurate results at the expense of computation time. With this concern, an Image Uncertainty-based Perspective-n-Point (IUPnP) with Fibonacci-based outlier rejection is proposed to accurately estimate the absolute pose of a calibrated camera with a minimum computation load. The uncertainties of the spherically normalized camera coordinates are formulated in the tangent space of the camera coordinate system and the initial pose is estimated using Singular Value Decomposition. The correspondences with tangent space residual exceeding the threshold values, are classified as outliers and then rejected iteratively. In order to prevent the inlier rejections and to improve the pose estimates, the threshold values are updated eventually using the Fibonacci technique. Finally, the estimated pose values are refined, using Gauss-Newton optimization. The proposed IUPnP algorithm is tested with synthetic data and real image data to validate its performance by comparing with the existing PnP algorithms in terms of accuracy. The results show that the proposed technique produces better pose estimates for correspondences with 50% outliers, than the state-of-art techniques do.