Real-time Monocular Object SLAM

We present a real-time object-based SLAM system that leverages the largest object database to date. Our approach comprises two main components: 1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and 2) a novel object recognition algorithm based on bags of binary words, which provides live detections with a database of 500 3D objects. The two components work together and benefit each other: the SLAM algorithm accumulates information from the observations of the objects, anchors object features to especial map landmarks and sets constrains on the optimization. At the same time, objects partially or fully located within the map are used as a prior to guide the recognition algorithm, achieving higher recall. We evaluate our proposal on five real environments showing improvements on the accuracy of the map and efficiency with respect to other state-of-the-art techniques

Texture analysis in an apple progeny through instrumental, sensory and histological phenotyping

Phenotypic analysis of texture traits was performed in an apple progeny by three complementary approaches: two classical instrumental measurements (compression and penetrometry), sensory assessment and histological screening. The progeny was composed of 141 individuals harvested over 2 years. Sensory and instrumental texture were assessed at harvest and after 2 and 4 months of cold storage. Histological screening was performed by combining macro-vision of outer parenchyma sections and image analysis on fruits after 2 months storage. Harvest year was observed to have a major impact on texture phenotypes followed by storage and genetic factors. Principal component analysis of data from the instrumental texture evaluations showed that the two methods complemented each other in characterizing the texture of the apple progeny. Compression parameters correlated better than penetrometry variables with sensory descriptors related to crispness, firmness, and graininess. Cell size distribution differentiated individuals in the apple progeny. It correlated with instrumental texture analyses and with juiciness perception. All measured texture related traits showed that they were all under genetic control with high heritability values. Higher values were obtained for fruits after 2 months storage. These results provide ground for future search of new apple texture QTLs

Variability of cell wall polysaccharides composition and hemicellulose enzymatic profile in an apple progeny

The genetic variability of apple cell walls polysaccharides chemical composition and structure was assessed in a progeny of 141 individuals harvested over 2 years. The variability of the hemicelluloses oligosaccharides released by glucanase was analyzed by MALDI-TOF MS. The genetic contribution was distinguished from harvest year as well as from parental crossing patterns and scab resistance selection. Results showed that harvest year had a major impact on cell wall polysaccharide composition and structure. Within each harvest, genetic effect impact more significantly cell wall polysaccharide chemistry than does reciprocal crossing or early scab selection. Uronic acids, glucose, galactose and xylose contents as well as some glucomannan and xyloglucan structures have a high heritability. This first cell wall chemotyping of an apple progeny opens the way for future searches of genetic markers for the chemical variability of cell wall polysaccharides

Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving

We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios. Instead of directly regressing the 3D bounding box using end-to-end approaches, we propose to use the easy-to-labeled 2D detection and discrete viewpoint classification together with a light-weight semantic inference method to obtain rough 3D object measurements. Based on the object-aware-aided camera pose tracking which is robust in dynamic environments, in combination with our novel dynamic object bundle adjustment (BA) approach to fuse temporal sparse feature correspondences and the semantic 3D measurement model, we obtain 3D object pose, velocity and anchored dynamic point cloud estimation with instance accuracy and temporal consistency. The performance of our proposed method is demonstrated in diverse scenarios. Both the ego-motion estimation and object localization are compared with the state-of-of-the-art solutions.Comment: 14 pages, 9 figures, eccv201

Experimental set-up for exciting and detecting magneto-optical effects and surface plasmon resonance simultaneously

We present here an experimental set-up system to excite and measure simultaneously surface plasmon resonance (SPR) and magneto-optic signal in hybrid magneto-plasmonic systems using two independent light sources. The system can be used to excite and measure both types of SPR, localized surface plasmons in nanostructures and surface plasmon polaritons in thin films. It also allows measuring SPR in presence of magnetic fields and recording magnetooptical hysteresis loops while exciting SPR

Multiwavelength optical observations of chromospherically active binary systems V. FF UMa (2RE J0933+624): a system with orbital period variation

This is the fifth paper in a series aimed at studying the chromospheres of active binary systems using several optical spectroscopic indicators to obtain or improve orbital solution and fundamental stellar parameters. We present here the study of FF UMa (2RE J0933+624), a recently discovered, X-ray/EUV selected, active binary with strong H_alpha emission. The objectives of this work are, to find orbital solutions and define stellar parameters from precise radial velocities and carry out an extensive study of the optical indicators of chromospheric activity. We obtained high resolution echelle spectroscopic observations during five observing runs from 1998 to 2004. We found radial velocities by cross correlation with radial velocity standard stars to achieve the best orbital solution. We also measured rotational velocity by cross-correlation techniques and have studied the kinematic by galactic space- velocity components (U, V, W) and Eggen criteria. Finally, we have determined the chromospheric contribution in optical spectroscopic indicators, from Ca II H & K to Ca II IRT lines, using the spectral subtraction technique. We have found that this system presents an orbital period variation, higher than previously detected in other RS CVn systems. We determined an improved orbital solution, finding a circular orbit with a period of 3.274 days. We derived the stellar parameters, confirming the subgiant nature of the primary component and obtained rotational velocities (vsini), of 33.57 km/s and 32.38 km/s for the primary and secondary components respectively. From our kinematic study, we can deduce its membership to the Castor moving group. Finally, the activity study has given us a better understanding of the possible mechanisms that produce the orbital period variation.Comment: Latex file with 16 pages, 18 figures. Available at http://www.ucm.es/info/Astrof/invest/actividad/actividad_pub.html Accepted for publication in: Astronomy & Astrophysics (A&A

Chromospheric activity and rotation of FGK stars in the solar vicinity. An estimation of the radial velocity jitter

Context: Chromospheric activity produces both photometric and spectroscopic variations that can be mistaken as planets. Large spots crossing the stellar disc can produce planet-like periodic variations in the light curve of a star. These spots clearly affect the spectral line profiles and their perturbations alter the line centroids creating a radial velocity jitter that might contaminate" the variations induced by a planet. Precise chromospheric activity measurements are needed to estimate the activity-induced noise that should be expected for a given star. Aims: We obtain precise chromospheric activity measurements and projected rotational velocities for nearby (d < 25 pc) cool (spectral types F to K) stars, to estimate their expected activity-related jitter. As a complementary objective, we attempt to obtain relationships between fluxes in different activity indicator lines, that permit a transformation of traditional activity indicators, i.e, CaII H & K lines, to others that hold noteworthy advantages. Methods: We used high resolution (~50000) echelle optical spectra. To determine the chromospheric emission of the stars in the sample, we used the spectral subtraction technique. Rotational velocities were determined using the cross-correlation technique. To infer activity-related radial velocity (RV) jitter, we used empirical relationships between this jitter and the R'_HK index. Results: We measured chromospheric activity, as given by different indicators throughout the optical spectra, and projected rotational velocities for 371 nearby cool stars. We have built empirical relationships among the most important chromospheric emission lines. Finally, we used the measured chromospheric activity to estimate the expected RV jitter for the active stars in the sample.Comment: Accepted for publication in Astronomy & Astrophysic

Plasmonic nanodevice with magnetic funcionalities: fabrication and characterization

We have designed and fabricated a nanodevice exhibiting simultaneously ferromagnetic properties of nanostructures with plasmonic properties of continuous films. Our device consists of an array of nanomagnets on top of a continuous plasmonic film. The patterned nanomagnets magnetic state is single domain and well-defined shape anisotropy. Despite the presence of the patterned media on top of the Au film, the system exhibits surface plasmon resonance characteristics of a continuous film, i.e., propagating surface plasmon-polaritons

Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings

To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features. This expense is further multiplied when a query image is evaluated against a gallery, e.g. in visual relocalization. While we don't obviate the need for geometric verification, we propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup. Our approach measures the asymmetric relation between two images. The model then learns a scene-specific measure of similarity, from training examples with known 3D visible-surface overlaps. The result is that we can quickly identify, for example, which test image is a close-up version of another, and by what scale factor. Subsequently, local features need only be detected at that scale. We validate our scene-specific model by showing how this embedding yields competitive image-matching results, while being simpler, faster, and also interpretable by humans.Comment: ECCV 202

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology