Efficient generic calibration method for general cameras with single centre of projection

Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method

Impact of lens distrortions on strain measurements obtained with digital image correlation

The determination of strain fields based on displacements obtained via DIC at the micro-strain level is still a cumbersome task. In particular when high-strain gradients are involved, e.g. in composite materials with multidirectional fibre reinforcement, uncertainties in the experimental setup and errors in the derivation of the displacement fields can substantially hamper the strain identification process. In this contribution, the aim is to investigate the impact of lens distortions on strain measurements. To this purpose, we first perform pure rigid body motion experiments, revealing the importance of precise correction of lens distortions. Next, a uni-axial tensile test on a textile composite with spatially varying high strain gradients is performed, resulting in very accurate determined strains along the fibers of the materia

CLASH: Mass Distribution in and around MACS J1206.2-0847 from a Full Cluster Lensing Analysis

We derive an accurate mass distribution of the galaxy cluster MACS J1206.2-0847 (z=0.439) from a combined weak-lensing distortion, magnification, and strong-lensing analysis of wide-field Subaru BVRIz' imaging and our recent 16-band Hubble Space Telescope observations taken as part of the Cluster Lensing And Supernova survey with Hubble (CLASH) program. We find good agreement in the regions of overlap between several weak and strong lensing mass reconstructions using a wide variety of modeling methods, ensuring consistency. The Subaru data reveal the presence of a surrounding large scale structure with the major axis running approximately north-west south-east (NW-SE), aligned with the cluster and its brightest galaxy shapes, showing elongation with a \sim 2:1 axis ratio in the plane of the sky. Our full-lensing mass profile exhibits a shallow profile slope dln\Sigma/dlnR\sim -1 at cluster outskirts (R>1Mpc/h), whereas the mass distribution excluding the NW-SE excess regions steepens further out, well described by the Navarro-Frenk-White form. Assuming a spherical halo, we obtain a virial mass M_{vir}=(1.1\pm 0.2\pm 0.1)\times 10^{15} M_{sun}/h and a halo concentration c_{vir} = 6.9\pm 1.0\pm 1.2 (\sim 5.7 when the central 50kpc/h is excluded), which falls in the range 4 <7 of average c(M,z) predictions for relaxed clusters from recent Lambda cold dark matter simulations. Our full lensing results are found to be in agreement with X-ray mass measurements where the data overlap, and when combined with Chandra gas mass measurements, yield a cumulative gas mass fraction of 13.7^{+4.5}_{-3.0}% at 0.7Mpc/h (\approx 1.7r_{2500}), a typical value observed for high mass clusters.Comment: Accepted by ApJ (30 pages, 17 figures), one new figure (Figure 10) added, minor text changes; a version with high resolution figures available at http://www.asiaa.sinica.edu.tw/~keiichi/upfiles/MACS1206/ms_highreso.pd

DPC-Net: Deep Pose Correction for Visual Localization

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a Deep Pose Correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane, Australia, May 21-25, 201