Accurate and Linear Time Pose Estimation from Points and Lines
The Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3D-to-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably est
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Department of Mathematics and Mechanics, Saint Petersburg University, Saint Petersburg, Russia [email protected] Institut de Rob` otica i Inform` atica Industrial, UPC-CSIC, Barcelona, Spain {jfunke,fmoreno}@iri.upc.edu
Abstract. The Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3D-to-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their pointbased counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.
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Introduction
The objective of the Perspective-n-Point problem (PnP) is to estimate the pose of a calibrated camera from n known 3D-to-2D point correspondences [34]. Early approaches were focused on solving the problem for the minimal cases with This work is partly funded by the Russian MES grant RFMEFI61516X0003; by the Spanish MINECO project RobInstruct TIN2014-58178-R and by the ERA-Net Chistera project I-DRESS PCIN-2015-147. Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46478-7 36) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VII, LNCS 9911, pp. 583–599, 2016. DOI: 10.1007/978-3-319-46478-7 36
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A. Vakhitov et al.
Fig. 1. Pose estimation results of OPnPL (left) and OPnP (right) in a scenario with a lack of reliable feature points. Blue points and solid line segments are detected in the image, and green dashed line segments are the model reference lines reprojected using the estimated pose. White lines are manually chosen on the 3D model to sketch its structure and projected onto the image to deem the quality of the estimated pose. Note also the shift along the line direction between the detected and the model lines. This issue needs to be handled in practice, where the reference lines in the model may only be partially detected on the image (due to partial occlusions). Image
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