skip to main content
Idioma:

Road features detection and sparse map-based vehicle localization in urban environments

Hata, Alberto Yukinobu

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação 2016-12-13

Acesso online. A biblioteca também possui exemplares impressos.

  • Título:
    Road features detection and sparse map-based vehicle localization in urban environments
  • Autor: Hata, Alberto Yukinobu
  • Orientador: Wolf, Denis Fernando
  • Assuntos: Mapa De Grade De Ocupação; Detecção De Guia; Detecção De Sinalização Horizontal; Localização De Monte Carlo; Localização De Veículos; Mapa De Ocupação De Processo Gaussiano; Road Marking Detection; Occupancy Grid Map; Monte Carlo Localization; Curb Detection; Gaussian Process Occupancy Map; Vehicle Localization
  • Notas: Tese (Doutorado)
  • Descrição: Localization is one of the fundamental components of autonomous vehicles by enabling tasks as overtaking, lane keeping and self-navigation. Urban canyons and bad weather interfere with the reception of GPS satellite signal which prohibits the exclusive use of such technology for vehicle localization in urban places. Alternatively, map-aided localization methods have been employed to enable position estimation without the dependence on GPS devices. In this solution, the vehicle position is given as the place that best matches the sensor measurement to the environment map. Before building the maps, feature sof the environment must be extracted from sensor measurements. In vehicle localization, curbs and road markings have been extensively employed as mapping features. However, most of the urban mapping methods rely on a street free of obstacles or require repetitive measurements of the same place to avoid occlusions. The construction of an accurate representation of the environment is necessary for a proper match of sensor measurements to the map during localization. To prevent the necessity of a manual process to remove occluding obstacles and unobserved areas, a vehicle localization method that supports maps built from partial observations of the environment is proposed. In this localization system,maps are formed by curb and road markings extracted from multilayer laser sensor measurements. Curb structures are detected even in the presence of vehicles that occlude the roadsides, thanks to the use of robust regression. Road markings detector employs Otsu thresholding to analyze infrared remittance data which makes the method insensitive to illumination. Detected road features are stored in two map representations: occupancy grid map (OGM) and Gaussian process occupancy map (GPOM). The first approach is a popular map structure that represents the environment through fine-grained grids. The second approach is a continuous representation that can estimate the occupancy of unseen areas. The Monte Carlo localization (MCL) method was adapted to support the obtained maps of the urban environment. In this sense, vehicle localization was tested in an MCL that supports OGM and an MCL that supports GPOM. Precisely, for MCL based on GPOM, a new measurement likelihood based on multivariate normal probability density function is formulated. Experiments were performed in real urban environments. Maps were built using sparse laser data to verify there ronstruction of non-observed areas. The localization system was evaluated by comparing the results with a high precision GPS device. Results were also compared with localization based on OGM.
  • DOI: 10.11606/T.55.2017.tde-08062017-090428
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação
  • Data de criação/publicação: 2016-12-13
  • Formato: Adobe PDF
  • Idioma: Inglês
Links
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  2 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.