Navigability estimation for autonomous vehicles using machine learning

• Ano de defesa: 2017
• Autor(a) principal: Mendes, Caio César Teodoro
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Aprendizado de máquina; Aprendizagem profunda; Deep learning; Detecção de obstáculos; Estimação de rua; Machine learning; Obstacle detection; Road detection; Stereo vision; Visão estéreo
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25092017-102021/

Autonomous navigation in outdoor, unstructured environments is one of the major challenges presents in the robotics field. One of its applications, intelligent autonomous vehicles, has the potential to decrease the number of accidents on roads and highways, increase the efficiency of traffic on major cities and contribute to the mobility of the disabled and elderly. For a robot/vehicle to safely navigate, accurate detection of navigable areas is essential. In this work, we address the task of visual road detection where, given an image, the objective is to classify its pixels into road or non-road. Instead of trying to manually derive an analytical solution for the task, we have used machine learning (ML) to learn it from a set of manually created samples. We have applied both traditional (shallow) and deep ML models to the task. Our main contribution regarding traditional ML models is an efficient and versatile way to aggregate spatially distant features, effectively providing a spatial context to such models. As for deep learning models, we have proposed a new neural network architecture focused on processing time and a new neural network layer called the semi-global layer, which efficiently provides a global context for the model. All the proposed methodology has been evaluated in the Karlsruhe Institute of Technology (KIT) road detection benchmark, achieving, in all cases, competitive results.