2D LIDAR Aided INS for vehicle positioning in urban environments

This paper presents a novel method to utilize\textit{2D} LIDAR for INS (Inertial Navigation System) aiding to improve\textit{3D} vehicle position estimation accuracy, especially when GNSS signals are shadowed.In the proposed framework, 2D LIDAR aiding is carried out without imposing any assumptions on the vehicle motion (e.g. we allow full six degree-of freedom motion).To achieve this, a closed-form formula is derived to predict the line measurement in the LIDAR's frame.This makes the feature association, residual formation and GUI display possible.With this formula, the Extended Kalman Filter (EKF) can be employed in a straightforward manner to fuse the LIDAR and IMU data to estimate the full state of the vehicle.Preliminary experimental results show the effectiveness of the LIDAR aiding in reducing the state estimation uncertainty along certain directions, when GNSS signals are shadowed

Analysis of methods for reducing line segments in maps: Towards a general approach

International audienceSegment-based maps are emerging as an efficient way to represent the environments in which mobile robots operate. When compared to grid-based maps, maps composed of line segments usually need less space to be stored. However, very little effort has been devoted to methods that allow to reduce the size of segment-based maps by removing redundant line segments that represent the same object in the environment. This problem is usually addressed with rather ad hoc methods that are embedded in mapping systems. In this paper, we put forward the problem of reducing the size of segment-based maps by presenting a survey of the existing methods and by experimentally evaluating some of them. Our results can be used to set out some guidelines for the development of a general approach to reducing redundant line segments in maps

Experimental validation of FastSLAM algorithm integrated with a linear features based map

International audienceIn this paper the Simultaneous Localization And Mapping (SLAM) problem in unknown indoor environments is addressed. A probabilistic approach integrating FastSLAM algorithm and a line feature map is developed and validated. Experi- mental validation is performed by a smart wheelchair equipped with proprioceptive and exteroceptive sensors in an office like environment where loop closing is achieved without any dedicated algorithm. Geometric hypothesis of orthogonal line features are considered to enhance the performance of the algorithm in the considered en- vironment. The proposed approach results in a computationally efficient solution to the SLAM problem and the high quality sensor measurements allow to main- tain a good localization of the mobile base and a compact representation of the environment

Data-efficient Learning of Robotic Clothing Assistance using Bayesian Gaussian Process Latent Variable Models

Motor-skill learning for complex robotic tasks is a challenging problem due to the high task variability. Robotic clothing assistance is one such challenging problem that can greatly improve the quality-of-life for the elderly and disabled. In this study, we propose a data-efficient representation to encode task-specific motor-skills of the robot using Bayesian nonparametric latent variable models. The effectivity of the proposed motor-skill representation is demonstrated in two ways: (1) through a real-time controller that can be used as a tool for learning from demonstration to impart novel skills to the robot and (2) by demonstrating that policy search reinforcement learning in such a task-specific latent space outperforms learning in the high-dimensional joint configuration space of the robot. We implement our proposed framework in a practical setting with a dual-arm robot performing clothing assistance tasks

On-line Independent Support Vector Machines for Cognitive Systems

Learning from experience and adapting to changing stimuli are fundamental capabilities for artificial cognitive systems. This calls for on-line learning methods able to achieve high accuracy while at the same time using limited computer power. Research on autonomous agents has been actively investigating these issues, mostly using probabilistic frameworks and within the context of navigation and learning by imitation. Still, recent results on robot localization have clearly pointed out the potential of discriminative classifiers for cognitive systems. In this paper we follow this approach and propose an on-line version of the Support Vector Machine (SVM) algorithm. Our method, that we call On-line Independent SVM, builds a solution on-line, achieving an excellent accuracy vs.~compactness trade-off. In particular the size of the obtained solution is always bounded, implying a bounded testing time. At the same time, the algorithm converges to the optimal solution at each incremental step, as opposed to similar approaches where optimality is achieved in the limit of infinite number of training data. These statements are supported by experiments on standard benchmark databases as well as on two real-world applications, namely $(a)$ place recognition by a mobile robot in an indoor environment, and $(b)$ human grasping posture classification

SLAM using Incremental Probabilistic PCA and Dimensionality Reduction

Abstract — The recent progress in robot mapping (or SLAM) algorithms has focused on estimating either point features (such as landmarks) or grid-based representations. Both of these representations generally scale with the size of the environment, not the complexity of the environment. Many thousand parameters may be required even when the structure of the environment can be represented using a few geometric primitives with many fewer parameters. We describe a novel SLAM model called IPSLAM; our algorithm clusters sensor data into line segments using the Probabilistic PCA algorithm, which provides a data likelihood model that can be used within a SLAM algorithm for the simultaneous estimation of map and robot pose parameters. Unlike previous work in extracting line-based representations from point-based maps, IPSLAM builds non-point-based maps directly from the sensor data. We demonstrate our algorithm on mapping part of the MI

Emissões atmosféricas de monóxido de carbono e óxidos de nitrogênio : um estudo de caso em indústrias licenciadas instaladas em Guarapuava-PR

Orientadora: Profa. Dra. Arislete Dantas de AquinoCoorientador: Prof. MSc. Mauricy KawanoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Meio Ambiente Urbano e Industrial, em parceria com o SENAI-PR e a Universität Stuttgart. Defesa : Curitiba, 17/11/2020Inclui referências: p. 98-112Resumo: A poluição atmosférica nos dias atuais é um grande desafio e trata-se de um grave problema ambiental. Levantamentos sobre emissões atmosféricas que sejam atualizados e disponibilizados frequentemente são escassos, o que compromete a sua aplicabilidade como instrumento de avaliação, gestão e licenciamento ambiental. O objetivo deste trabalho é apresentar um inventário de emissões atmosféricas de material particulado (MP), monóxido de carbono (CO) e óxidos de nitrogênio (NOx) de indústrias licenciadas instaladas em Guarapuava-PR e a elaboração de mapas de níveis de poluição atmosférica, para apresentá-los como ferramenta de gestão ambiental. Dados de fontes de emissão foram obtidos por meio de licenças ambientais dos empreendimentos. As cargas dos poluentes foram estimadas por meio dos fatores de emissão do AP-42. A distribuição espacial das fontes de emissão e respectivos níveis de poluição estimados, foi efetuada em um Sistema de Informação Geográfica (SIG). Para a geração dos mapas de calor foi adotada a densidade de Kernel. O método geoestatístico de Kriging permitiu a interpolação entre dados de poluição e localização. Uma Análise de Componentes Principais (ACP) foi utilizada para avaliar a distribuição dos valores das cargas de MP, CO e NOx. Foram obtidas informações de 174 fontes emissoras de poluentes atmosféricos, distribuídas entre fontes fugitivas (39%) e pontuais (61%). As estimativas de emissões ocorreram para 46 fontes pontuais, as quais dispunham de informações apropriadas em suas licenças ambientais, resultando em cargas totais de CO de 298,36 kg/h, para NOx 989,93 kg/h e para MP 243,07 kg/h. As maiores cargas de MP e NOx estiveram associadas às usinas de asfalto que empregam óleo de xisto na combustão e de CO à indústria de papel e celulose que utiliza a madeira. Apesar da predominância no uso de materiais derivados de madeira na combustão industrial, a discrepância evidenciada para as emissões de MP e NOx, vinculadas ao uso do óleo de xisto, indicou o elevado potencial poluidor deste combustível. A maior concentração das fontes pontuais ocorreu na região urbana, contudo, as áreas mais críticas de nível de MP, CO e NOx previstas, encontram-se em sua maioria, fora do perímetro urbano. Ainda assim foram evidenciados receptores de maior sensibilidade (centros educacionais, unidades de saúde, áreas verdes e praças) que estão situados sob influência relevante de fontes de emissão na área urbana. A análise dos resultados ressalta a importância de se considerar a variável espacial quando se trata de poluição atmosférica, para evitar que se desprezem aspectos relevantes. Ficou evidente que o elevado potencial poluidor de um empreendimento pode superar o grau de impacto de um aglomerado de fontes. O produto obtido neste estudo é proposto como ferramenta para avaliar a condição locacional de novos empreendimentos e contribuir para decisões coerentes sobre novos processos de licenciamento e gestão ambiental local. Este material tem potencial para auxiliar em investigações de conformidade, decisões em licenciamentos ambientais e no estabelecimento de critérios de permissão de operação industrial.Abstract: Air pollution today is a major challenge and a serious environmental problem. Surveys on atmospheric emissions that are frequently updated and made available are scarce, compromising their applicability as an instrument for environmental assessment, management, and licensing. The objective of this work is to present an inventory of atmospheric emissions of particulate matter (PM), carbon monoxide (CO), and nitrogen oxides (NOx) from licensed industries installed in Guarapuava-PR and the preparation of maps of atmospheric pollution levels to present them as an environmental management tool. Data on emission sources were obtained through environmental project licenses. Pollutant loads were estimated using the AP-42 emission factors. Were used a Geographic Information System (GIS) for the spatial distribution of the emission sources and respective levels of pollution estimated. Kernel density was adopted for the generation of heatmaps. Kriging's geostatistical method allowed the interpolation between pollution and location data. A Principal Component Analysis (PCA) was used to assess PM, CO, and NOx loads' distribution values. Information was obtained from 174 sources of air pollutants, distributed among fugitive (39%) and punctual sources (61%). Emissions estimates were made for 46 punctual sources, which had appropriate information in their environmental licenses, resulting in 298.36 kg/h for CO, 989.93 kg/h for NOx, and 243.07 kg/h for PM. The highest charge of PM and NOx were associated with asphalt plants that use shale oil for combustion and CO to the paper and cellulose industry that uses wood. Despite the predominance of using wood-derived materials in industrial combustion, the discrepancy evidenced for PM and NOx emissions, linked to the use of shale oil, indicated the high polluting potential of this fuel. The highest concentration of punctual sources occurred in the urban region. However, the most critical PM, CO, and NOx levels foreseen are mostly located outside the urban perimeter. Even so, receptors of greater sensitivity (educational centers, health units, green areas, and squares) were found to be located under relevant influence from emission sources in the urban area. The results' analysis highlights the importance of considering the spatial variable for atmospheric pollution to avoid neglecting relevant aspects. It was evident that the high polluting potential of an enterprise can overcome the degree of impact of a cluster of sources. The product obtained in this study is proposed as a tool to assess the locational condition of new projects and contribute to coherent decisions about new licensing and local environmental management processes. This material has the potential to assist in compliance investigations, environmental licensing decisions, and the establishment of industrial operation permission criteria