1,720,966 research outputs found
An Effective Multi-Cue Positioning System for Agricultural Robotics
Full text linkThe self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper
D2CO: Fast and Robust Registration of 3D Textureless Objects Using the Directional Chamfer Distance
No full textThis paper introduces a robust and efficient vision based method for object detection and 3D pose estimation that exploits a novel edge-based registration algorithm we called Direct Directional Chamfer Optimization (D2CO). Our approach is able to handle textureless and partially occluded objects and does not require any off-line object learning step. Depth edges and visible patterns extracted from the 3D CAD model of the object are matched against edges detected in the current grey level image by means of a 3D distance transform represented by an image tensor, that encodes the minimum distance to an edge point in a joint direction/location space. D2CO refines the object position employing a non-linear optimization procedure, where the cost being minimized is extracted directly from the 3D image tensor. Differently from other popular registration algorithms as ICP, that require to constantly update the correspondences between points, our approach does not require any iterative re-association step: the data association is implicitly optimized while inferring the object position. This enables D2CO to obtain a considerable gain in speed over other registration algorithms while presenting a wider basin of convergence. We tested our system with a set of challenging untextured objects in presence of occlusions and cluttered background, showing accurate results and often outperforming other state-of-the-art methods
An Effective One-shot Body Part Multi-View Reconstruction Device with Self-calibration Capabilities
Full text linkThis paper introduces a custom-built low-cost camera ring device designed for automatic cast synthesis, able to accurately and instantly scan body parts. The scanned mesh will be used as a backbone model for the cast design and 3D printing. The system is based on the multi-view active stereo principle and it is composed of a circular array of 16 synchronized cameras (Fig. 1) and 4 equally distributed IR pseudo-random laser pattern projectors. We employ a custom multi-view stereo reconstruction pipeline based on (Schönberger et al., 2016), which guarantees optimal results without the downsides of the supervised data-driven multi-view stereo algorithms, i.e. data collection and ground truth labeling. Additionally, inspired by (Duda and Frese, 2018), we propose a novel, automated calibration system to extract intrinsic and extrinsic camera parameters which are required to perform robust multi-view stereo reconstructions
FlexSight - A Flexible and Accurate System for Object Detection and Localization for Industrial Robots
Full text linkWe present a novel smart camera - the FlexSight C1 - designed to enable an industrial robot to detect and localize several types of objects and parts in an accurate and reliable way. The C1 integrates all the sensors and a powerful mini computer with a complete Operating System running robust 3D reconstruction and object localization algorithms on-board, so it can be directly connected to the robot that is guided directly by the device during the production cycle without any external computers in the loop. In this paper, we describe the FlexSight C1 hardware configuration along with the algorithms designed to face the model based localization problem of textureless objects, namely: (1) an improved version of the PatchMatch Stereo matching algorithm for depth estimation; (2) an object detection pipeline based on deep transfer learning with synthetic data. All the presented algorithms have been tested on publicly available datasets, showing effective results and improved runtime performance
Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU
Full text linkSelf-driving vehicles and autonomous ground robots require a reliable and
accurate method to analyze the traversability of the surrounding environment
for safe navigation. This paper proposes and evaluates a real-time machine
learning-based Traversability Analysis method that combines geometric features
with appearance-based features in a hybrid approach based on a SVM classifier.
In particular, we show that integrating a new set of geometric and visual
features and focusing on important implementation details enables a noticeable
boost in performance and reliability. The proposed approach has been compared
with state-of-the-art Deep Learning approaches on a public dataset of outdoor
driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying
complexity, demonstrating its effectiveness and robustness. The method runs
fully on CPU and reaches comparable results with respect to the other methods,
operates faster, and requires fewer hardware resources.Comment: Accepted to 17th International Conference on Intelligent Autonomous
Systems (IAS-17
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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