1,720,959 research outputs found
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation
No full textThis work proposes a novel deep network architecture to solve the camera ego-motion estimation problem. A motion estimation network generally learns features similar to optical flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an autoencoder network to find a nonlinear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture latent space visual odometry (LS-VO). The experiments show that LS-VO achieves a considerable increase in performances with respect to baselines, while the number of parameters of the estimation network only slightly increases
Towards monocular digital elevation model (DEM) estimation by convolutional neural networks - Application on synthetic aperture radar images
No full textSynthetic aperture radar (SAR) interferometry (InSAR) is performed using repeat-pass geometry. InSAR technique is used to estimate the topographic reconstruction of the earth surface. The main problem of the range-Doppler focusing technique is the nature of the two-dimensional SAR result, affected by the layover indetermination. In order to resolve this problem, a minimum of two sensor acquisitions, separated by a baseline and extended in the cross-slant-range, are needed. However, given its multi-temporal nature, these techniques are vulnerable to atmosphere and Earth environment parameters variation in addition to physical platform instabilities. Furthermore, either two radars are needed or an interferometric cycle is required (that spans from days to weeks), which makes real time DEM estimation impossible. In this work, the authors propose a novel experimental alternative to the InSAR method that uses single-pass acquisitions, using a data driven approach implemented by Deep Neural Networks. We propose a fully Convolutional Neural Network (CNN) Encoder-Decoder architecture, training it on radar images in order to estimate DEMs from single pass image acquisitions. Our results on a set of Sentinel images show that this method is able to learn to some extent the statistical properties of the DEM. The results of this exploratory analysis are encouraging and open the way to the solution of single-pass DEM estimation problem with data driven approaches
Towards monocular digital elevation model (DEM) estimation by convolutional neural networks - Application on synthetic aperture radar images
No full textSynthetic aperture radar (SAR) interferometry (InSAR) is performed using repeat-pass geometry. InSAR technique is used to estimate the topographic reconstruction of the earth surface. The main problem of the range-Doppler focusing technique is the nature of the two-dimensional SAR result, affected by the layover indetermination. In order to resolve this problem, a minimum of two sensor acquisitions, separated by a baseline and extended in the cross-slant-range, are needed. However, given its multi-temporal nature, these techniques are vulnerable to atmosphere and Earth environment parameters variation in addition to physical platform instabilities. Furthermore, either two radars are needed or an interferometric cycle is required (that spans from days to weeks), which makes real time DEM estimation impossible. In this work, the authors propose a novel experimental alternative to the InSAR method that uses single-pass acquisitions, using a data driven approach implemented by Deep Neural Networks. We propose a fully Convolutional Neural Network (CNN) Encoder-Decoder architecture, training it on radar images in order to estimate DEMs from single pass image acquisitions. Our results on a set of Sentinel images show that this method is able to learn to some extent the statistical properties of the DEM. The results of this exploratory analysis are encouraging and open the way to the solution of single-pass DEM estimation problem with data driven approaches
A Preliminary Experimental Analysis of V-tail Quad-Rotor Dynamics
No full textStandard quad-rotors are the most common and versatile Unmanned Aerial Vehicles (UAVs) thanks to their simple control and mechanics. However other configurations with distinct capabilities exist. We present a preliminary systematic study of an alternative configuration known as V-tail, which is still mechanically simple, with four fixed rotors, but whose back rotors are tilted by a known angle. Mathematical modelling and field experiments suggest that this configuration is able to achieve better performance in manoeuvring control, while losing some power only in the stationary hovering task. In addition, these increases in performance are obtained with the same attitude control of the standard quad-rotor, making this configuration very easy to setup
A Transfer Learning Approach for Multi-Cue Semantic Place Recognition
No full textAs researchers are continuously striving for developing robotic systems able to move into the ’the wild’, the interest towards novel learning paradigms for domain adaptation has increased. In the specific application of semantic place recognition from cameras, supervised learning algorithms are typically adopted. However, once learning have been performed, if the robot is moved to another location, the acquired knowledge may be not useful, as the novel scenario can be very different from the old one. The obvious solution would be to retrain the model updating the robot internal representation of the environment. Unfortunately this procedure involves a very time consuming data-labeling effort at the human side. To avoid these issues, in this paper we propose a novel transfer learning approach for place categorization from visual cues. With our method the robot is able to decide automatically if and how much its internal knowledge is useful in the novel scenario. Differently from previous approaches, we consider the situation where the old and the novel scenario may differ significantly (not only the visual room appearance changes but also different room categories are present). Importantly, our approach does not require labeling from a human operator. We also propose a strategy for improving the performance of the proposed method optimally fusing two complementary visual cues. Our extensive experimental evaluation demonstrates the advantages of our approach on several sequences from publicly available datasets.As researchers are continuously striving for de- veloping robotic systems able to move into the ’the wild’, the interest towards novel learning paradigms for domain adaptation has increased. In the specific application of semantic place recognition from cameras, supervised learning algorithms are typically adopted. However, once learning have been per- formed, if the robot is moved to another location, the acquired knowledge may be not useful, as the novel scenario can be very different from the old one. The obvious solution would be to retrain the model updating the robot internal representation of the environment. Unfortunately this procedure involves a very time consuming data-labeling effort at the human side. To avoid these issues, in this paper we propose a novel transfer learning approach for place categorization from visual cues. With our method the robot is able to decide automatically if and how much its internal knowledge is useful in the novel scenario. Differently from previous approaches, we consider the situation where the old and the novel scenario may differ significantly (not only the visual room appearance changes but also different room categories are present). Importantly, our approach does not require labeling from a human operator. We also propose a strategy for improving the performance of the proposed method optimally fusing two complementary visual cues. Our extensive experimental evaluation demonstrates the advantages of our approach on several sequences from publicly available datasets
Transfer Learning for Visual Place Classification
No full textA fundamental challenge in mobile robotics is to
provide robots the capability to move autonomously in real
world unconstrained scenarios. In the recent years this led to an
increased interest towards novel learning paradigms for domain
adaptation. In this paper we specifically consider the problem of
visual place recognition. Current semantic place categorization
approaches typically rely on supervised learning methods. This
implies a time consuming human labeling effort. Moreover, once
learning has been performed, if the environmental conditions
vary or the robot is moved to another location, the learned model
may not be useful, as the novel scenario can be very different
from the old one. To avoid these issues, we propose a novel
transfer learning approach for visual place recognition. With
our method the robot is only given some training data, eventually
collected in different scenarios by other robots, and is able to
decide autonomously if and how much this knowledge is useful
in the current scenario. Differently from previous approaches,
our method keeps the human annotation effort to the minimum
and, thanks to the adoption of a transfer risk measure, is able
to quantify automatically the similarity between the old and the
novel scenario. The experimental results on publicly available
datasets demonstrate the effectiveness of our approach
Visual-inertial Tracking on Android for Augmented Reality Applications
No full textAugmented Reality (AR) aims to enhance a person’s
vision of the real world with useful information about the
surrounding environment. Amongst all the possible applications,
AR systems can be very useful as visualization tools for structural
and environmental monitoring. While the large majority of AR
systems run on a laptop or on a head-mounted device, the
advent of smartphones have created new opportunities. One
of the most important functionality of an AR system is the
ability of the device to self localize. This can be achieved
through visual odometry, a very challenging task for smartphone.
Indeed, on most of the available smartphone AR applications,
self localization is achieved through GPS and/or inertial sensors.
Hence, developing an AR system on a mobile phone also poses
new challenges due to the limited amount of computational
resources. In this paper we describe the development of a
egomotion estimation algorithm for an Android smartphone. We
also present an approach based on an Extended Kalman Filter for
improving localization accuracy integrating the information from
inertial sensors. The implemented solution achieves a localization
accuracy comparable to the PC implementation while running
on an Android device.Augmented Reality (AR) aims to enhance a person’s vision of the real world with useful information about the surrounding environment. Amongst all the possible applications, AR systems can be very useful as visualization tools for structural and environmental monitoring. While the large majority of AR systems run on a laptop or on a head-mounted device, the advent of smartphones have created new opportunities. One of the most important functionality of an AR system is the ability of the device to self localize. This can be achieved through visual odometry, a very challenging task for smartphone. Indeed, on most of the available smartphone AR applications, self localization is achieved through GPS and/or inertial sensors. Hence, developing an AR system on a mobile phone also poses new challenges due to the limited amount of computational resources. In this paper we describe the development of a egomotion estimation algorithm for an Android smartphone. We also present an approach based on an Extended Kalman Filter for improving localization accuracy integrating the information from inertial sensors. The implemented solution achieves a localization accuracy comparable to the PC implementation while running on an Android device
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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