Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology

Urban intersection classification: a comparative analysis

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.European Commissio

Métodos de classificação confiável e resiliente de movimentos de membros superiores baseado em extreme learning machines e sinais de eletromiografia de superfície

Apesar de avanços recentes, a classificação confiável de sinais de eletromiografia de superfície (sEMG) permanece uma tarefa árdua sob a perspectiva de Aprendizagem de Máquina. Sinais de sEMG possuem uma sobreposição de classes inerente à sua natureza, o que impede a separação perfeita das amostras e produz ruídos de classificação. Alternativas ao problema geralmente baseiam-se na filtragem do sEMG ou métodos de pós-processamento como o Major-Voting, soluções estas que necessariamente geram atrasos na classificação do sinal e frequentemente não geram melhoras substanciais. A abordagem deste trabalho baseia-se no desenvolvimento de métodos confiáveis e resilientes sob a perspectiva de classificação que gerem saídas mais estáveis e consistentes para o classificador baseado em Extreme Learning Machines (ELM) utilizado. Para tanto, métodos envolvendo o pré-processamento e pós-processamento, a suavização do arg max do classificador, thresholds adaptativos e um classificador binário auxiliar foram utilizados. Os sinais classificados derivam de 12 canais de sEMG envolvendo três bases de dados diferentes onde 99 ensaios compostos pela execução de 17 movimentos distintos do segmento mão-braço foram realizados. Nos melhores resultados, os métodos utilizados atingiram taxas de acerto médio global de 66,99 ± 23,6% para a base de voluntários amputados, 87,10 ± 5,89% para a base de voluntários não-amputados e taxas superiores a 99% para todas as variações de diferentes ensaios que compõe a base de dados adquirida em laboratório. Já para a taxa de acerto média ponderada por classes, nos melhores resultados foram de 53,36 ± 18,2% para a base de voluntários amputados, 77,94 ± 6,22% para a base de voluntários não-amputados e taxas superiores a 91% para os ensaios da base de dados adquirida em laboratório. Ambas as métricas de taxa de acerto consideradas superam ou equivalem-se a alternativas descritas na literatura, utilizando abordagens que não demandam grandes mudanças estruturais no classificador.Despite recent advances, reliable classification of surface electromyography (sEMG) signals remains an arduous task from the perspective of Machine Learning. sEMG signals have inherent class overlaps that prevent optimal labeling due to classification noises. Alternatives to classification ripples usually rely on stochastic sEMG filtering or post-processing methods, like Major-Voting, both solutions that insert constraints and additional delays in signal classification and often do not generate substantial improvements. The approach of this paper focuses on the development of reliable and resilient methods used in combination with an Extreme Learning Machines (ELM) classifier to generate more stable and consistent outputs. Methods of pre-processing and post-processing, a smoothed arg max version of the ELM, adaptive thresholds, and an auxiliary binary classifier were used to process signals derived from 12 EMG channels from three different databases. In total, 99 trials were performed, each one containing 17 different upper-limb movements. The proposed methods reached an average overall accuracy rate of 66.99 ± 23.6% for the amputee individuals’ database, 87.10 ± 5.89% for the non-amputee individuals’ database, and rates over 99% for all variations of our own lab-generated database. The average weighted accuracy rates were 53.36 ± 18.2% for the amputee individuals’ database, 77.94 ± 6.22% for the base of the non-amputee individuals’ database, and higher than 91% for the best-case scenario of our own lab-generated database. In both metrics considered, the results outperform, or match alternatives described in the literature using approaches that do not require significant changes in the classifier's architecture