Publication

Uncertainty-Aware Camera Pose Estimation from Points and Lines

Conference Article

Conference

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Edition

2021

Pages

4657-4666

Doc link

https://doi.org/10.1109/CVPR46437.2021.00463

File

Download the digital copy of the doc pdf document

Abstract

Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP and DLS for the uncertainty-aware pose estimation. We also modify motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset.

Categories

computer vision.

Scientific reference

A. Vakhitov, L. Ferraz, A. Agudo and F. Moreno-Noguer. Uncertainty-Aware Camera Pose Estimation from Points and Lines, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Nashville, TN, USA (Virtual), pp. 4657-4666, Computer Vision Foundation.