Detection of the navigable road limits by analysis of the accumulated point cloud density
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/28205 |
Resumo: | As part of the Atlas project, this dissertation aims to identify the navigable limits of the road by analyzing the density of accumulated point clouds, obtained through laser readings from a SICK LD-MRS sensor. This sensor, installed in front of the AtlasCar2, has the purpose of identifying obstacles at road level and from it the creation of occupation grids that delimit the navigable space of the vehicle is proposed. First, the point cloud density is converted into an occupancy density grid, normalized in each frame in relation to the maximum density. Edge detection algorithms and gradient filters are subsequently applied to the density grid, in order to detect patterns that match sudden changes in density, both positive and negative. To these grids are applied thresholds in order to remove irrelevant information. Finally, a methodology for quantitative evaluation of algorithms was also developed, using KML files to define road boundaries and, relying on the accuracy of the GPS data obtained, comparing the actual navigable space with the one obtained by the methodology for detection of road boundaries and thus evaluating the performance of the work developed. In this work, the results of the different algorithms are presented, as well as several tests taking into account the influence of grid resolution, car speed, among others. In general, the work developed meets the initially proposed objectives, being able to detect both positive and negative obstacles and being minimally robust to speed and road conditions. |
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Detection of the navigable road limits by analysis of the accumulated point cloud densityLIDARPerceptionCurb DetectionRoad LimitsAtlasCar2Point CloudDensityROSGradientEdge DetectionAs part of the Atlas project, this dissertation aims to identify the navigable limits of the road by analyzing the density of accumulated point clouds, obtained through laser readings from a SICK LD-MRS sensor. This sensor, installed in front of the AtlasCar2, has the purpose of identifying obstacles at road level and from it the creation of occupation grids that delimit the navigable space of the vehicle is proposed. First, the point cloud density is converted into an occupancy density grid, normalized in each frame in relation to the maximum density. Edge detection algorithms and gradient filters are subsequently applied to the density grid, in order to detect patterns that match sudden changes in density, both positive and negative. To these grids are applied thresholds in order to remove irrelevant information. Finally, a methodology for quantitative evaluation of algorithms was also developed, using KML files to define road boundaries and, relying on the accuracy of the GPS data obtained, comparing the actual navigable space with the one obtained by the methodology for detection of road boundaries and thus evaluating the performance of the work developed. In this work, the results of the different algorithms are presented, as well as several tests taking into account the influence of grid resolution, car speed, among others. In general, the work developed meets the initially proposed objectives, being able to detect both positive and negative obstacles and being minimally robust to speed and road conditions.No âmbito do projeto Atlas, esta dissertação prevê a identificação dos limites navegáveis da estrada através da análise da densidade da acumulaçao de nuvens de pontos, obtidas através de leituras laser provenientes de um sensor SICK LD-MRS. Este sensor, instalado na frente do AtlasCar2, tem como propósito a identificação de obstáculos ao nível da estrada e a partir dos seus dados prevê-se a criação de grelhas de ocupação que delimitem o espaço navegável do veículo. Em primeiro lugar, a densidade da nuvem de pontos é transformada numa grelha de densidade normalizada em cada frame em relação à densidade máxima, à qual posteriormente são aplicados algoritmos de deteção de arestas e filtros de gradiente com o objetivo de detetar padrões que correspondam a mudanças súbitas de densidade, tanto positivas como negativas. A estas grelhas são aplicados limiares de forma a eliminar informação irrelevante. Por fim, foi desenvolvida também uma metodologia de avaliação quantitativa dos algoritmos, usando ficheiros KML para deliniar limites da estrada e, contanto com a precisão dos dados de GPS obtidos, comparar o espaço navegável real com o obtido pela metodologia de deteção de limites de estrada e assim avaliar o desempenho dos algoritmos desenvolvidos. Neste trabalho são apresentados resultados dos diferentes algoritmos, bem como diversos testes tendo em conta a influência da resolução de grelha, velocidade do carro, entre outros. O trabalho desenvovido cumpre os objetivos propostos inicialmente, sendo capaz de detetar ambos obstáculos positivos e negativos e sendo minimamente robusto a velocidade e condições de estrada.2019-07-22T00:00:00Z2019-07-22T00:00:00Z2019-07-22info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/28205engRato, Daniela Ferreira Pinto Diasinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:54:35Zoai:ria.ua.pt:10773/28205Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:00:48.697614Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
title |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
spellingShingle |
Detection of the navigable road limits by analysis of the accumulated point cloud density Rato, Daniela Ferreira Pinto Dias LIDAR Perception Curb Detection Road Limits AtlasCar2 Point Cloud Density ROS Gradient Edge Detection |
title_short |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
title_full |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
title_fullStr |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
title_full_unstemmed |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
title_sort |
Detection of the navigable road limits by analysis of the accumulated point cloud density |
author |
Rato, Daniela Ferreira Pinto Dias |
author_facet |
Rato, Daniela Ferreira Pinto Dias |
author_role |
author |
dc.contributor.author.fl_str_mv |
Rato, Daniela Ferreira Pinto Dias |
dc.subject.por.fl_str_mv |
LIDAR Perception Curb Detection Road Limits AtlasCar2 Point Cloud Density ROS Gradient Edge Detection |
topic |
LIDAR Perception Curb Detection Road Limits AtlasCar2 Point Cloud Density ROS Gradient Edge Detection |
description |
As part of the Atlas project, this dissertation aims to identify the navigable limits of the road by analyzing the density of accumulated point clouds, obtained through laser readings from a SICK LD-MRS sensor. This sensor, installed in front of the AtlasCar2, has the purpose of identifying obstacles at road level and from it the creation of occupation grids that delimit the navigable space of the vehicle is proposed. First, the point cloud density is converted into an occupancy density grid, normalized in each frame in relation to the maximum density. Edge detection algorithms and gradient filters are subsequently applied to the density grid, in order to detect patterns that match sudden changes in density, both positive and negative. To these grids are applied thresholds in order to remove irrelevant information. Finally, a methodology for quantitative evaluation of algorithms was also developed, using KML files to define road boundaries and, relying on the accuracy of the GPS data obtained, comparing the actual navigable space with the one obtained by the methodology for detection of road boundaries and thus evaluating the performance of the work developed. In this work, the results of the different algorithms are presented, as well as several tests taking into account the influence of grid resolution, car speed, among others. In general, the work developed meets the initially proposed objectives, being able to detect both positive and negative obstacles and being minimally robust to speed and road conditions. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-22T00:00:00Z 2019-07-22T00:00:00Z 2019-07-22 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/28205 |
url |
http://hdl.handle.net/10773/28205 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137663826001920 |