Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.This work has been supported by Grant DPI2009-07144 and DPI2013-40534-R from Ministerio de Ciencia e Innovacion of the Spanish Government, University of Alicante Projects GRE09-16 and GRE10-35, and Valencian Government Project GV/2011/034

Kilo-instruction processors: overcoming the memory wall

Historically, advances in integrated circuit technology have driven improvements in processor microarchitecture and led to todays microprocessors with sophisticated pipelines operating at very high clock frequencies. However, performance improvements achievable by high-frequency microprocessors have become seriously limited by main-memory access latencies because main-memory speeds have improved at a much slower pace than microprocessor speeds. Its crucial to deal with this performance disparity, commonly known as the memory wall, to enable future high-frequency microprocessors to achieve their performance potential. To overcome the memory wall, we propose kilo-instruction processors-superscalar processors that can maintain a thousand or more simultaneous in-flight instructions. Doing so means designing key hardware structures so that the processor can satisfy the high resource requirements without significantly decreasing processor efficiency or increasing energy consumption.Peer ReviewedPostprint (published version

Geometric 3D point cloud compression

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment but usually the huge amount of 3D information is unmanageable by the robot storage and computing capabilities. A data compression is necessary to store and manage this information but preserving as much information as possible. In this paper, we propose a 3D lossy compression system based on plane extraction which represent the points of each scene plane as a Delaunay triangulation and a set of points/area information. The compression system can be customized to achieve different data compression or accuracy ratios. It also supports a color segmentation stage to preserve original scene color information and provides a realistic scene reconstruction. The design of the method provides a fast scene reconstruction useful for further visualization or processing tasks.This work has been supported by the Spanish Government DPI2013-40534-R grant

3D Maps Representation Using GNG

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.This work was partially funded by the Spanish Government DPI2013-40534-R grant

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.This work was partially funded by the Spanish Government DPI2013-40534-R grant

Abnormal expression of cerebrospinal fluid cation chloride cotransporters in patients with Rett Syndrome

Objective: Rett Syndrome is a progressive neurodevelopmental disorder caused mainly by mutations in the gene encoding methyl-CpG-binding protein 2. The relevance of MeCP2 for GABAergic function was previously documented in animal models. In these models, animals show deficits in brain-derived neurotrophic factor, which is thought to contribute to the pathogenesis of this disease. Neuronal Cation Chloride Cotransporters (CCCs) play a key role in GABAergic neuronal maturation, and brain-derived neurotrophic factor is implicated in the regulation of CCCs expression during development. Our aim was to analyse the expression of two relevant CCCs, NKCC1 and KCC2, in the cerebrospinal fluid of Rett syndrome patients and compare it with a normal control group. Methods: The presence of bumetanide sensitive NKCC1 and KCC2 was analysed in cerebrospinal fluid samples from a control pediatric population (1 day to 14 years of life) and from Rett syndrome patients (2 to 19 years of life), by immunoblot analysis. Results: Both proteins were detected in the cerebrospinal fluid and their levels are higher in the early postnatal period. However, Rett syndrome patients showed significantly reduced levels of KCC2 and KCC2/NKCC1 ratio when compared to the control group. Conclusions: Reduced KCC2/NKCC1 ratio in the cerebrospinal fluid of Rett Syndrome patients suggests a disturbed process of GABAergic neuronal maturation and open up a new therapeutic perspective

Magnetic zeolites: novel nanoreactors through radiofrequency heating

Many catalytic applications use conventional heating to increase the temperature to allow the desired reaction. A novel methodology is presented for the preparation of magnetic zeolite-based catalysts, allowing more efficient radiofrequency heating. These nanoreactors are tested in the isomerisation of citronellal with successful results and without any apparent deactivation

¿Quién puede participar? Un análisis documental acerca de la participación de la infancia en cuidados alternativos

Child participation in protection systems is a fundamental right that entails multiple psychosocial benefits for children and adolescents. However, its correct implementation in protection systems is a challenge to be addressed. In Chile, research on child participation, particularly in the national child protection system (SENAME), is scarce. This study seeks to explore, under the model of meaningful participation, how child participation is considered in alternative care programs and their regulatory and legal frameworks, through a thematic documentary analysis. The findings point to a low level of definition of the concept participation in technical and legal regulations, and a tendency to assign children and adolescents to a passive role, leaving adults to define the moments and forms of participation. Some factors,such as the age of the child, which play a central role in making participation possible are analysed. In conclusion, meaningful participation is not guaranteed in its three dimensions (being informed, listened to and considered in decision-making) throughout the process, being relegated to isolated and variable instances depending on the different programs.La participación infantil en los sistemas de protección es un derecho fundamental que implica múltiples beneficios psicosociales para los niños, niñas y adolescentes (NNA). No obstante, su correcta implementación en los sistemas proteccionales es un desafío a tratar. En Chile, la investigación en participación infantil, y en específico en el sistema de protección infantil nacional, SENAME, es escasa. Este estudio busca explorar de qué manera la participación infantil significativa del NNA es considerada en programas de cuidados alternativos de SENAME y sus marcos normativos y legales, a través de un análisis temático documental. Los hallazgos apuntan a un bajo nivel de definición del concepto de participación en normativas técnicas y legales, y una tendencia a asignar a los NNA a un rol pasivo, donde son los funcionarios de cada programa quienes tienen mayor propositividad a la hora de definir los momentos y formas de participación. Factores como la edad del NNA parecen tomar un rol central a la hora de permitir una participación más independiente. La participación significativa no se garantiza en sus tres dimensiones a lo largo del proceso, y éstas se presentan de manera irregular

A Comparative Study of Registration Methods for RGB-D Video of Static Scenes

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.This work has been supported by a grant from the Spanish Government, DPI2013-40534-R, University of Alicante projects GRE11-01 and a grant from the Valencian Government, GV/2013/005