RIDI: Robust IMU Double Integration

This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research

DeepFactors: Real-time probabilistic dense monocular SLAM

The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approaches have been proposed that vary in scene geometry representation (sparse landmarks, dense maps), the consistency metric used for optimising the multi-view problem, and the use of learned priors. We present a SLAM system that unifies these methods in a probabilistic framework while still maintaining real-time performance. This is achieved through the use of a learned compact depth map representation and reformulating three different types of errors: photometric, reprojection and geometric, which we make use of within standard factor graph software. We evaluate our system on trajectory estimation and depth reconstruction on real-world sequences and present various examples of estimated dense geometry

Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments

For robotic inspection tasks in known environments fiducial markers provide a reliable and low-cost solution for robot localization. However, detection of such markers relies on the quality of RGB camera data, which degrades significantly in the presence of visual obscurants such as fog and smoke. The ability to navigate known environments in the presence of obscurants can be critical for inspection tasks especially, in the aftermath of a disaster. Addressing such a scenario, this work proposes a method for the design of fiducial markers to be used with thermal cameras for the pose estimation of aerial robots. Our low cost markers are designed to work in the long wave infrared spectrum, which is not affected by the presence of obscurants, and can be affixed to any object that has measurable temperature difference with respect to its surroundings. Furthermore, the estimated pose from the fiducial markers is fused with inertial measurements in an extended Kalman filter to remove high frequency noise and error present in the fiducial pose estimates. The proposed markers and the pose estimation method are experimentally evaluated in an obscurant filled environment using an aerial robot carrying a thermal camera.Comment: 10 pages, 5 figures, Published in International Symposium on Visual Computing 201

ElasticFusion: real-time dense SLAM and light source estimation

We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments and beyond explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any post-processing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency. In the spirit of improving map quality as well as tracking accuracy and robustness, we furthermore explore a novel approach to real-time discrete light source detection. This technique is capable of detecting numerous light sources in indoor environments in real-time as a user handheld camera explores the scene. Absolutely no prior information about the scene or number of light sources is required. By making a small set of simple assumptions about the appearance properties of the scene our method can incrementally estimate both the quantity and location of multiple light sources in the environment in an online fashion. Our results demonstrate that our technique functions well in many different environments and lighting configurations. We show that this enables (a) more realistic augmented reality (AR) rendering; (b) a richer understanding of the scene beyond pure geometry and; (c) more accurate and robust photometric trackin

MonoSLAM: Real-time single camera SLAM

Published versio

Analysis of Paramyxovirus Transcription and Replication by High-Throughput Sequencing.

We have developed a high-throughput sequencing (HTS) workflow for investigating paramyxovirus transcription and replication. We show that sequencing of oligo(dT)-selected polyadenylated mRNAs, without considering the orientation of the RNAs from which they had been generated, cannot accurately be used to analyze the abundance of viral mRNAs because genomic RNA copurifies with the viral mRNAs. The best method is directional sequencing of infected cell RNA that has physically been depleted of ribosomal and mitochondrial RNA followed by bioinformatic steps to differentiate data originating from genomes from viral mRNAs and antigenomes. This approach has the advantage that the abundance of viral mRNA (and antigenomes) and genomes can be analyzed and quantified from the same data. We investigated the kinetics of viral transcription and replication during infection of A549 cells with parainfluenza virus type 2 (PIV2), PIV3, PIV5, or mumps virus and determined the abundances of individual viral mRNAs and readthrough mRNAs. We found that the mRNA abundance gradients differed significantly between all four viruses but that for each virus the pattern remained relatively stable throughout infection. We suggest that rapid degradation of non-poly(A) mRNAs may be primarily responsible for the shape of the mRNA abundance gradient in parainfluenza virus 3, whereas a combination of this factor and disengagement of RNA polymerase at intergenic sequences, particularly those at the NP:P and P:M gene boundaries, may be responsible in the other viruses.IMPORTANCE High-throughput sequencing (HTS) of virus-infected cells can be used to study in great detail the patterns of virus transcription and replication. For paramyxoviruses, and by analogy for all other negative-strand RNA viruses, we show that directional sequencing must be used to distinguish between genomic RNA and mRNA/antigenomic RNA because significant amounts of genomic RNA copurify with poly(A)-selected mRNA. We found that the best method is directional sequencing of total cell RNA, after the physical removal of rRNA (and mitochondrial RNA), because quantitative information on the abundance of both genomic RNA and mRNA/antigenomes can be simultaneously derived. Using this approach, we revealed new details of the kinetics of virus transcription and replication for parainfluenza virus (PIV) type 2, PIV3, PIV5, and mumps virus, as well as on the relative abundance of the individual viral mRNAs

Complete genome sequences of elephant endotheliotropic herpesviruses 1A and 1B determined directly from fatal cases

A highly lethal hemorrhagic disease associated with infection by elephant endotheliotropic herpesvirus (EEHV) poses a severe threat to Asian elephant husbandry. We have used high-throughput methods to sequence the genomes of the two genotypes that are involved in most fatalities, namely EEHV1A and EEHV1B (species Elephantid herpesvirus 1, genus Proboscivirus, subfamily Betaherpesvirinae, family Herpesviridae). The sequences were determined from postmortem tissue samples, despite the data containing tiny proportions of viral reads among reads from a host for which the genome sequence was not available. The EEHV1A genome is 180,421 bp in size and consists of a unique sequence (174,601 bp) flanked by a terminal direct repeat (2,910 bp). The genome contains 116 predicted protein-coding genes, of which six are fragmented, and seven paralogous gene families are present. The EEHV1B genome is very similar to that of EEHV1A in structure, size, and gene layout. Half of the EEHV1A genes lack orthologs in other members of subfamily Betaherpesvirinae, such as human cytomegalovirus (genus Cytomegalovirus) and human herpesvirus 6A (genus Roseolovirus). Notable among these are 23 genes encoding type 3 membrane proteins containing seven transmembrane domains (the 7TM family) and seven genes encoding related type 2 membrane proteins (the EE50 family). The EE50 family appears to be under intense evolutionary selection, as it is highly diverged between the two genotypes, exhibits evidence of sequence duplications or deletions, and contains several fragmented genes. The availability of the genome sequences will facilitate future research on the epidemiology, pathogenesis, diagnosis, and treatment of EEHV-associated disease

The switch between acute and persistent paramyxovirus infection caused by single amino acid substitutions in the RNA polymerase P subunit

Paramyxoviruses can establish persistent infections both in vitro and in vivo, some of which lead to chronic disease. However, little is known about the molecular events that contribute to the establishment of persistent infections by RNA viruses. Using parainfluenza virus type 5 (PIV5) as a model we show that phosphorylation of the P protein, which is a key component of the viral RNA polymerase complex, determines whether or not viral transcription and replication becomes repressed at late times after infection. If the virus becomes repressed, persistence is established, but if not, the infected cells die. We found that single amino acid changes at various positions within the P protein switched the infection phenotype from lytic to persistent. Lytic variants replicated to higher titres in mice than persistent variants and caused greater infiltration of immune cells into infected lungs but were cleared more rapidly. We propose that during the acute phases of viral infection in vivo, lytic variants of PIV5 will be selected but, as the adaptive immune response develops, variants in which viral replication can be repressed will be selected, leading to the establishment of prolonged, persistent infections. We suggest that similar selection processes may operate for other RNA viruses

Pairwise Decomposition of Image Sequences for Active Multi-View Recognition

A multi-view image sequence provides a much richer capacity for object recognition than from a single image. However, most existing solutions to multi-view recognition typically adopt hand-crafted, model-based geometric methods, which do not readily embrace recent trends in deep learning. We propose to bring Convolutional Neural Networks to generic multi-view recognition, by decomposing an image sequence into a set of image pairs, classifying each pair independently, and then learning an object classi- fier by weighting the contribution of each pair. This allows for recognition over arbitrary camera trajectories, without requiring explicit training over the potentially infinite number of camera paths and lengths. Building these pairwise relationships then naturally extends to the next-best-view problem in an active recognition framework. To achieve this, we train a second Convolutional Neural Network to map directly from an observed image to next viewpoint. Finally, we incorporate this into a trajectory optimisation task, whereby the best recognition confidence is sought for a given trajectory length. We present state-of-the-art results in both guided and unguided multi-view recognition on the ModelNet dataset, and show how our method can be used with depth images, greyscale images, or both