Vida y pensamiento de Juan O´Gorman

1 archivo PDF (10 páginas). Con la autorización de Bettina Cetto."... análisis de la Biblioteca Central [de la Universidad Nacional Autónoma de México] ... La nuestra será necesariamente una aproximación más personal, porque nos tocó ser testigos -parte en tiempo real, parte a posteriori- de la construcción del edificio de la Biblioteca y sobre todo, de su decoración. Conocimos la Biblioteca a través de Juan O’Gorman. Para nosotras siempre ha sido la biblioteca de Juan, nuestro padrino. Escribir ahora sobre ella nos causa una enorme emoción, porque es una invitación a recrear muchos momentos gratos en que Juan compartía apasionadamente los avatares de su trabajo plástico con la familia. Pero para entender y apreciar mejor la obra en su contexto no nos queda más que incluir también una selección -si bien algo azarosa- de datos biográficos y reflexiones acerca de la vida y el pensamiento de O’Gorman.

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Graphene: A Context-Preserving Open Information Extraction System

We introduce Graphene, an Open IE system whose goal is to generate accurate, meaningful and complete propositions that may facilitate a variety of downstream semantic applications. For this purpose, we transform syntactically complex input sentences into clean, compact structures in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them in order to maintain their semantic relationship. In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.Comment: 27th International Conference on Computational Linguistics (COLING 2018

Cinco arquitectos y un mosaico

1 archivo PDF (7 páginas) : incluye 4 imágenes"Yo creo que las cuatro figuras dispuestas en el centro del mural de mosaico representan varias cosas. Destaca que cada una está orientada hacia un punto cardinal. Son cuatro hombres que construyen, pues cada uno sostiene herramientas de trabajo. Y cada uno está formado por piedritas y mosaicos de diferente color con relación a los otros tres, lo que yo imagino representa a las distintas razas de la tierra. Pero el dibujo de estos cuatro hombres entrelazados es también un signo de la masonería que Max toma de la Edad Media, más concretamente, del cuaderno de un arquitecto itinerante llamado Villard de Honnecourt, que ha pasado a la historia debido a que se ha conservado un cuaderno de viajes que le perteneció: 33 páginas de pergamino con 250 dibujos, fechado entre 1220 y 1240, y conservado en la Biblioteca National de París. ..." Palabras clave: Mural. Casa Max Cetto. Calle de Agua 130. Pedregal de San Ángel, México, Distrito Federal. (1949).http://www.arts-history.mx/blog/index.php/component/k2/item/2574-cinco-arquitectos-y-un-mosaic

Graphene: Semantically-Linked Propositions in Open Information Extraction

We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.Comment: 27th International Conference on Computational Linguistics (COLING 2018

Visual servoing of aerial manipulators

The final publication is available at link.springer.comThis chapter describes the classical techniques to control an aerial manipulator by means of visual information and presents an uncalibrated image-based visual servo method to drive the aerial vehicle. The proposed technique has the advantage that it contains mild assumptions about the principal point and skew values of the camera, and it does not require prior knowledge of the focal length, in contrast to traditional image-based approaches.Peer ReviewedPostprint (author's final draft

Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper describes a self-calibration procedure that jointly estimates the extrinsic parameters of an exteroceptive sensor able to observe ego-motion, and the intrinsic parameters of an odometry motion model, consisting of wheel radii and wheel separation. We use iterative nonlinear onmanifold optimization with a graphical representation of the state, and resort to an adaptation of the pre-integration theory, initially developed for the IMU motion sensor, to be applied to the differential drive motion model. For this, we describe the construction of a pre-integrated factor for the differential drive motion model, which includes the motion increment, its covariance, and a first-order approximation of its dependence with the calibration parameters. As the calibration parameters change at each solver iteration, this allows a posteriori factor correction without the need of re-integrating the motion data. We validate our proposal in simulations and on a real robot and show the convergence of the calibration towards the true values of the parameters. It is then tested online in simulation and is shown to accommodate to variations in the calibration parameters when the vehicle is subject to physical changes such as loading and unloading a freight.Peer ReviewedPostprint (author's final draft

Pose-graph SLAM sparsification using factor descent

Since state of the art simultaneous localization and mapping (SLAM) algorithms are not constant time, it is often necessary to reduce the problem size while keeping as much of the original graph’s information content. In graph SLAM, the problem is reduced by removing nodes and rearranging factors. This is normally faced locally: after selecting a node to be removed, its Markov blanket sub-graph is isolated, the node is marginalized and its dense result is sparsified. The aim of sparsification is to compute an approximation of the dense and non-relinearizable result of node marginalization with a new set of factors. Sparsification consists on two processes: building the topology of new factors, and finding the optimal parameters that best approximate the original dense distribution. This best approximation can be obtained through minimization of the Kullback-Liebler divergence between the two distributions. Using simple topologies such as Chow-Liu trees, there is a closed form for the optimal solution. However, a tree is oftentimes too sparse and produces bad distribution approximations. On the contrary, more populated topologies require nonlinear iterative optimization. In the present paper, the particularities of pose-graph SLAM are exploited for designing new informative topologies and for applying the novel factor descent iterative optimization method for sparsification. Several experiments are provided comparing the proposed topology methods and factor descent optimization with state-of-the-art methods in synthetic and real datasets with regards to approximation accuracy and computational cost.Peer ReviewedPostprint (author's final draft