Loop Closing for Visual Pose Tracking during Close-Range 3-D Modeling

This work deals with the passive tracking of the pose of a close-range 3-D modeling device using its own high-rate images in realtime, concurrently with customary 3-D modeling of the scene by laser triangulation. Our former works in Refs. [1,2] successfully implemented visual pose tracking. Accuracy being a central requirement to 3-D modeling, however, here we note that accuracy can be further increased using a graph-based nonlinear optimization of the tracked pose by minimization of reprojection errors. Loop closures e.g. when having scanned all around the objects provide the opportunity to increase pose tracking and 3-D modeling accuracy. The sparse optimization is in the form of a hybrid, keyframe-based bundle adjustment algorithm on stereo keyframes, yielding rapid optimization of the whole trajectory and object mesh model within a second. The optimization is supported by the use of appearance-based SURF descriptors together with a bank of parallel three-point-perspective pose solvers

Conjugate Gradient Bundle Adjustment

Bundle adjustment for multi-view reconstruction is traditionally done using the Levenberg-Marquardt algorithm with a direct linear solver, which is computationally very expensive. An alternative to this approach is to apply the conjugate gradients algorithm in the inner loop. Tins is appealing since the main computational step of the CG algorithm involves only a simple matrix-vector multiplication with the Jacobian. In this work we improve on the latest published approaches to bundle adjustment with conjugate gradients by making full use of the least squares nature of the problem. We employ an easy-to-compute QR factorization based block preconditioner and show how a certain property of the preconditioned system allows us to reduce the work per iteration to roughly half of the standard CG algorithm

Inference of missing data in photovoltaic monitoring datasets

This is an Open Access Article. It is published by IET publishing under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/Photovoltaic (PV) systems are frequently covered by performance guarantees, which are often based on attaining a certain performance ratio (PR). Climatic and electrical data are collected on site to verify that these guarantees are met or that the systems are working well. However, in-field data acquisition commonly suffers from data loss, sometimes for prolonged periods of time, making this assessment impossible or at the very best introducing significant uncertainties. This study presents a method to mitigate this issue based on back-filling missing data. Typical cases of data loss are considered and a method to infer this is presented and validated. Synthetic performance data is generated based on interpolated environmental data and a trained empirical electrical model. A case study is subsequently used to validate the method. Accuracy of the approach is examined by creating artificial data loss in two closely monitored PV modules. A missing month of energy readings has been replenished, reproducing PR with an average daily and monthly mean bias error of about −1 and −0.02%, respectively, for a crystalline silicon module. The PR is a key property which is required for the warranty verification, and the proposed method yields reliable results in order to achieve this