Accurate and linear time pose estimation from points and lines

The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-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 point-based 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.Peer ReviewedPostprint (author's final draft

Fast optical source for quantum key distribution based on semiconductor optical amplifiers

A novel integrated optical source capable of emitting faint pulses with different polarization states and with different intensity levels at 100 MHz has been developed. The source relies on a single laser diode followed by four semiconductor optical amplifiers and thin film polarizers, connected through a fiber network. The use of a single laser ensures high level of indistinguishability in time and spectrum of the pulses for the four different polarizations and three different levels of intensity. The applicability of the source is demonstrated in the lab through a free space quantum key distribution experiment which makes use of the decoy state BB84 protocol. We achieved a lower bound secure key rate of the order of 3.64 Mbps and a quantum bit error ratio as low as $1.14\times 10^{-2}$ while the lower bound secure key rate became 187 bps for an equivalent attenuation of 35 dB. To our knowledge, this is the fastest polarization encoded QKD system which has been reported so far. The performance, reduced size, low power consumption and the fact that the components used can be space qualified make the source particularly suitable for secure satellite communication

Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU

Localization in challenging, natural environments, such as forests or woodlands, is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this letter, we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from three-dimensional point clouds by comparing triplets (anchor, positive, and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored toward loop closure detection resulting in a small model that can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payloads, such as drones, quadrupeds, or Unmanned Ground Vehicles (UGVs)

SKD: keypoint detection for point clouds using saliency estimation

We present SKD, a novel keypoint detector that uses saliency to determine the best candidates from a point cloud for tasks such as registration and reconstruction. The approach can be applied to any differentiable deep learning descriptor by using the gradients of that descriptor with respect to the 3D position of the input points as a measure of their saliency. The saliency is combined with the original descriptor and context information in a neural network, which is trained to learn robust keypoint candidates. The key intuition behind this approach is that keypoints are not extracted solely as a result of the geometry surrounding a point, but also take into account the descriptor's response. The approach was evaluated on two large LIDAR datasets - the Oxford RobotCar dataset and the KITTI dataset, where we obtain up to 50% improvement over the state-of-the-art in both matchability and repeatability. When performing sparse matching with the keypoints computed by our method we achieve a higher inlier ratio and faster convergence

CYK4 inhibits Rac1-dependent PAK1 and ARHGEF7 effector pathways during cytokinesis.

In mitosis, animal cells lose their adhesion to the surrounding surfaces and become rounded. During mitotic exit, they reestablish these adhesions and at the same time physically contract and divide. How these competing processes are spatially segregated at the cell cortex remains mysterious. To address this question, we define the specific effector pathways used by RhoA and Rac1 in mitotic cells. We demonstrate that the MKlp1-CYK4 centralspindlin complex is a guanosine triphosphatase-activating protein (GAP) for Rac1 and not RhoA and that CYK4 negatively regulated Rac1 activity at the cell equator in anaphase. Cells expressing a CYK4 GAP mutant had defects in cytokinesis and showed elevated staining for the cell adhesion marker vinculin. These defects could be rescued by depletion of ARHGEF7 and p21-activated kinase, Rac1-specific effector proteins required for cell adhesion. Based on these findings, we propose that CYK4 GAP activity is required during anaphase to inhibit Rac1-dependent effector pathways associated with control of cell spreading and adhesion

Universal membrane-labeling combined with expression of Katushka far-red fluorescent protein enables non-invasive dynamic and longitudinal quantitative 3D dual-color fluorescent imaging of multiple bacterial strains in mouse intestine

Background: The human gastrointestinal (GI) tract microbiota has been a subject of intense research throughout the 3rd Millennium. Now that a general picture about microbiota composition in health and disease is emerging, questions about factors determining development of microbiotas with specific community structures will be addressed. To this end, usage of murine models for colonization studies remains crucial. Optical in vivo imaging of either bioluminescent or fluorescent bacteria is the basis for non-invasive detection of intestinal colonization of bacteria. Although recent advances in in vivo fluorescence imaging have overcome many limitations encountered in bioluminescent imaging of intestinal bacteria, such as requirement for live cells, high signal attenuation and 2D imaging, the method is still restricted to bacteria for which molecular cloning tools are available. Results: Here, we present usage of a lipophilic fluorescent dye together with Katushka far-red fluorescent protein to establish a dual-color in vivo imaging system to monitor GI transit of different bacterial strains, suitable also for strains resistant to genetic labeling. Using this system, we were able to distinguish two different E. coli strains simultaneously and show their unique transit patterns. Combined with fluorescence molecular tomography, these distinct strains could be spatially and temporally resolved and quantified in 3D. Conclusions: Developed novel method for labeling microbes and identify their passage both temporally and spatially in vivo makes now possible to monitor all culturable bacterial strains, also those that are resistant to conventional genetic labeling.Peer reviewe

New lives for old reverse osmosis (RO) membranes

The overall objective of the present work was to estimate by membrane autopsy the level of performances degradation of old reverse osmosis (RO) membranes/modules in order to envisage their reuse A mechanistic approach using the Spiegler-Kedem-Karchalsky model helps us to observe that the old RO membrane acquired a convective mass transfer We defined a novel Peclet number (denoted Pe') usable to distinguish between diffusional and convective mass transfer We observed that for the old membrane Pe' numbers are always higher than 1. Furthermore, SEM/AFM and EDX experiments show crystals and bacteria particles as fouling agents, roughness increase for the old membrane (from 73 to 220 nm) due to foulants deposition EDX experiments demonstrated that FexOy crystals are dominant SP measurements help us to observe a displacement of the isoelectric point (IEP) for the virgin membrane from 2.7 +/- 0.3 to 4 6 +/- 0 3 in comparison to the old one showing chemical modifications of the inner active layer suspected due to biofouling residuals In the last part and for the first time. module materials reuse Occurred in the place of their incineration as geotextile in proper home garden. Crown Copyright (C) 2009 Published by Elsevier B V All rights reserved