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Author |
Miguel Realpe; Boris X. Vintimilla; L. Vlacic |
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Title |
Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. |
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2015 |
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IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. |
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253-258 |
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Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system. |
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cidis @ cidis @ |
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37 |
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Author |
Dennys Paillacho; Cecilio Angulo; Marta Díaz. |
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Title |
An Exploratory Study of Group-Robot Social Interactions in a Cultural Center |
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2015 |
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IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, International Conference on, Hamburg, Germany, 2015 |
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This article describes an exploratory study of social human-robot interaction with the experimental robotic platform MASHI. The experiences were carried out in La B`obila Cultural Center in Barcelona, Spain to study the visitor preferences, characterize the groups and their spatial relationships in this open and unstructured environment. Results showed that visitors prefers to play and dialogue with the robot. Children have the highest interest in interacting with the robot, more than young and adult visitors. Most of the groups consisted of more than 3 visitors, however the size of the groups during interactions was continuously changed. In static situations, the observed spatial relationships denotes a social cohesion in the human-robot interactions. |
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gtsi @ user @ |
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67 |
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Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Scene representations for autonomous driving: an approach based on polygonal primitives |
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2015 |
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Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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417 |
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503-515 |
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Scene reconstruction, Point cloud, Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Springer International Publishing Switzerland 2016 |
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Second Iberian Robotics Conference |
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cidis @ cidis @ |
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45 |
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Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Learning crossspectral similarity measures with deep convolutional neural networks |
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2016 |
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IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
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267-275 |
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The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. |
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cidis @ cidis @ |
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48 |
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