LiDAR Enhanced Structure-from-Motion

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.Comment: 6 pages plus reference. Work has been submitted to ICRA 202

A novel method for high-throughput detection and quantification of neutrophil extracellular traps reveals ROS-independent NET release with immune complexes

AbstractA newly-described first-line immune defence mechanism of neutrophils is the release of neutrophil extracellular traps (NETs). Immune complexes (ICxs) induce low level NET release. As such, the in vitro quantification of NETs is challenging with current methodologies. In order to investigate the role of NET release in ICx-mediated autoimmune diseases, we developed a highly sensitive and automated method for quantification of NETs. After labelling human neutrophils with PKH26 and extracellular DNA with Sytox green, cells are fixed and automatically imaged with 3-dimensional confocal laser scanning microscopy (3D-CLSM). NET release is then quantified with digital image analysis whereby the NET amount (Sytox green area) is corrected for the number of imaged neutrophils (PKH26 area). A high sensitivity of the assay is achieved by a) significantly augmenting the area of the well imaged (11%) as compared to conventional assays (0.5%) and b) using a 3D imaging technique for optimal capture of NETs, which are topologically superimposed on neutrophils. In this assay, we confirmed low levels of NET release upon human ICx stimulation which were positive for citrullinated histones and neutrophil elastase. In contrast to PMA-induced NET release, ICx-induced NET release was unchanged when co-incubated with diphenyleneiodonium (DPI). We were able to quantify NET release upon stimulation with serum from RA and SLE patients, which was not observed with normal human serum. To our knowledge, this is the first semi-automated assay capable of sensitive detection and quantification of NET release at a low threshold by using 3D CLSM. The assay is applicable in a high-throughput manner and allows the in vitro analysis of NET release in ICx-mediated autoimmune diseases

Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild

The fusion of multi-modal sensors has become increasingly popular in autonomous driving and intelligent robots since it can provide richer information than any single sensor, enhance reliability in complex environments. Multi-sensor extrinsic calibration is one of the key factors of sensor fusion. However, such calibration is difficult due to the variety of sensor modalities and the requirement of calibration targets and human labor. In this paper, we demonstrate a new targetless cross-modal calibration framework by focusing on the extrinsic transformations among stereo cameras, thermal cameras, and laser sensors. Specifically, the calibration between stereo and laser is conducted in 3D space by minimizing the registration error, while the thermal extrinsic to the other two sensors is estimated by optimizing the alignment of the edge features. Our method requires no dedicated targets and performs the multi-sensor calibration in a single shot without human interaction. Experimental results show that the calibration framework is accurate and applicable in general scenes.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

VoxDet: Voxel Learning for Novel Instance Detection

Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel representation and reliable voxel matching mechanism. VoxDet first ingeniously proposes template voxel aggregation (TVA) module, effectively transforming multi-view 2D images into 3D voxel features. By leveraging associated camera poses, these features are aggregated into a compact 3D template voxel. In novel instance detection, this voxel representation demonstrates heightened resilience to occlusion and pose variations. We also discover that a 3D reconstruction objective helps to pre-train the 2D-3D mapping in TVA. Second, to quickly align with the template voxel, VoxDet incorporates a Query Voxel Matching (QVM) module. The 2D queries are first converted into their voxel representation with the learned 2D-3D mapping. We find that since the 3D voxel representations encode the geometry, we can first estimate the relative rotation and then compare the aligned voxels, leading to improved accuracy and efficiency. Exhaustive experiments are conducted on the demanding LineMod-Occlusion, YCB-video, and the newly built RoboTools benchmarks, where VoxDet outperforms various 2D baselines remarkably with 20% higher recall and faster speed. To the best of our knowledge, VoxDet is the first to incorporate implicit 3D knowledge for 2D tasks.Comment: 17 pages, 10 figure

Loop Corrections and Naturalness in a Chiral Effective Field Theory

The loop expansion is applied to a chiral effective hadronic lagrangian; with the techniques of Infrared Regularization, it is possible to separate out the short-range contributions and to write them as local products of fields that are already present in our lagrangian. (The appropriate field variables must be re-defined at each order in loops.) The corresponding parameters implicitly include short-range effects to all orders in the interaction, so these effects need not be calculated explicitly. The remaining (long-range) contributions that must be calculated are nonlocal and resemble those in conventional nuclear-structure calculations. Nonlinear isoscalar scalar $(\sigma)$ and vector $(\omega)$ meson interactions are included, which incorporate many-nucleon forces and nucleon substructure. Calculations are carried out at the two-loop level to illustrate these techniques at finite nuclear densities and to verify that the coupling parameters remain natural when fitted to the empirical properties of equilibrium nuclear matter. Contributions from the $\omega N$ tensor coupling are also discussed.Comment: 22 pages, 6 figure

Multi objective H∞ active anti-roll bar control for heavy vehicles

DOI : 10.1016/j.ifacol.2017.08.2071International audienceIn the active anti-roll bar control on heavy vehicles, roll stability and energy consumption of actuators are two essential but conflicting performance objectives. In a previous work, the authors proposed an integrated model, including four electronic servo-valve hydraulic actuators in a linear yaw-roll model on a single unit heavy vehicle. This paper aims to design an H ∞ active anti-roll bar control and solves a Multi-Criteria Optimization (MCO) problem by using Genetic Algorithms (GAs) to select the weighting functions for the H ∞ synthesis. Thanks to GAs, the roll stability and the energy consumption are handled using a single high level parameter and illustrated via the Pareto optimality. Simulation results in frequency and time domains emphasize the efficiency of the use of the GAs method for a MCO problem in H ∞ active anti-roll bar control on heavy vehicles

The heterogeneous effect of software patents on expected returns: evidence from India

We contribute to the literature on the role of patenting for economic development by analyzing the impact of patent protection for software in India. We find that a proposed broadening of patent eligibility to include software in 2004 had a large positive effect on average returns for listed software companies in India. An unanticipated reversal of this proposed policy change in 2005 resulted in substantial negative returns. We illustrate substantial heterogeneity in the dynamics of these effects across the sequence of events. We also find smaller firms to have been systematically and most significantly affected by the tightening of patent law with regard to software patents