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\BOOKMARK [0][-]{chapter*.2}{Resumen}{}% 1
\BOOKMARK [0][-]{chapter*.3}{Abstract}{}% 2
\BOOKMARK [0][-]{chapter*.4}{Resumen extendido}{}% 3
\BOOKMARK [0][-]{chapter*.5}{\315ndice general}{}% 4
\BOOKMARK [0][-]{chapter*.6}{\315ndice de figuras}{}% 5
\BOOKMARK [0][-]{chapter*.7}{\315ndice de tablas}{}% 6
\BOOKMARK [0][-]{chapter*.8}{\315ndice de listados de c\363digo fuente}{}% 7
\BOOKMARK [0][-]{chapter*.9}{\315ndice de algoritmos}{}% 8
\BOOKMARK [0][-]{chapter*.11}{Lista de acr\363nimos}{}% 9
\BOOKMARK [0][-]{chapter.12}{1 Introducci\363n}{}% 10
\BOOKMARK [1][-]{section.13}{1.1 Sistemas de conducci\363n aut\363nomos}{chapter.12}% 11
\BOOKMARK [1][-]{section.16}{1.2 Sistemas de percepci\363n}{chapter.12}% 12
\BOOKMARK [2][-]{subsection.17}{1.2.1 Principales sensores para la percepci\363n en veh\355culos aut\363nomos}{section.16}% 13
\BOOKMARK [3][-]{subsubsection.18}{1.2.1.1 C\341mara}{subsection.17}% 14
\BOOKMARK [3][-]{subsubsection.21}{1.2.1.2 Radar}{subsection.17}% 15
\BOOKMARK [3][-]{subsubsection.23}{1.2.1.3 LiDAR}{subsection.17}% 16
\BOOKMARK [2][-]{subsection.25}{1.2.2 Sistemas de detecci\363n}{section.16}% 17
\BOOKMARK [2][-]{subsection.28}{1.2.3 Sistemas de seguimiento}{section.16}% 18
\BOOKMARK [2][-]{subsection.30}{1.2.4 Fusi\363n sensorial}{section.16}% 19
\BOOKMARK [1][-]{section.32}{1.3 Deep Learning}{chapter.12}% 20
\BOOKMARK [0][-]{chapter.34}{2 Propuesta de trabajo}{}% 21
\BOOKMARK [0][-]{chapter.35}{3 Sistemas cl\341sicos de percepci\363n con LiDAR}{}% 22
\BOOKMARK [1][-]{section.36}{3.1 Voxelizaci\363n}{chapter.35}% 23
\BOOKMARK [1][-]{section.41}{3.2 RANSAC-3D}{chapter.35}% 24
\BOOKMARK [1][-]{section.46}{3.3 KD-tree}{chapter.35}% 25
\BOOKMARK [1][-]{section.60}{3.4 Filtrado previo y posterior a la detecci\363n}{chapter.35}% 26
\BOOKMARK [0][-]{chapter.61}{4 Sistemas de percepci\363n con LiDAR basados en Deep Learning}{}% 27
\BOOKMARK [1][-]{section.62}{4.1 Principales datasets}{chapter.61}% 28
\BOOKMARK [2][-]{subsection.63}{4.1.1 KITTI}{section.62}% 29
\BOOKMARK [3][-]{subsubsection.66}{4.1.1.1 An\341lisis de la estructura del GT y las PCLs de KITTI}{subsection.63}% 30
\BOOKMARK [2][-]{subsection.69}{4.1.2 Waymo}{section.62}% 31
\BOOKMARK [2][-]{subsection.72}{4.1.3 nuScenes}{section.62}% 32
\BOOKMARK [3][-]{subsubsection.76}{4.1.3.1 NuScenes devkit}{subsection.72}% 33
\BOOKMARK [2][-]{subsection.95}{4.1.4 Comparativa entre los diferentes datasets}{section.62}% 34
\BOOKMARK [1][-]{section.97}{4.2 Estado del arte en t\351cnicas de detecci\363n utilizando LiDAR}{chapter.61}% 35
\BOOKMARK [2][-]{subsection.98}{4.2.1 SECOND}{section.97}% 36
\BOOKMARK [2][-]{subsection.106}{4.2.2 PointPillars}{section.97}% 37
\BOOKMARK [2][-]{subsection.111}{4.2.3 PointRCNN}{section.97}% 38
\BOOKMARK [2][-]{subsection.116}{4.2.4 PV-RCNN}{section.97}% 39
\BOOKMARK [2][-]{subsection.122}{4.2.5 CBGS}{section.97}% 40
\BOOKMARK [1][-]{section.127}{4.3 OpenPCDet}{chapter.61}% 41
\BOOKMARK [0][-]{chapter.129}{5 Desarrollo realizado}{}% 42
\BOOKMARK [1][-]{section.130}{5.1 Estado del proyecto Techs4AgeCar}{chapter.129}% 43
\BOOKMARK [2][-]{subsection.131}{5.1.1 Robot Operating System}{section.130}% 44
\BOOKMARK [2][-]{subsection.134}{5.1.2 Docker}{section.130}% 45
\BOOKMARK [2][-]{subsection.136}{5.1.3 CARLA}{section.130}% 46
\BOOKMARK [2][-]{subsection.139}{5.1.4 Desarrollo en el proyecto Techs4AgeCar}{section.130}% 47
\BOOKMARK [2][-]{subsection.142}{5.1.5 Arquitectura del proyecto Techs4AgeCar}{section.130}% 48
\BOOKMARK [1][-]{section.150}{5.2 Implementaci\363n en CARLA}{chapter.129}% 49
\BOOKMARK [2][-]{subsection.151}{5.2.1 Funcionamiento del LiDAR en CARLA}{section.150}% 50
\BOOKMARK [2][-]{subsection.153}{5.2.2 Implementaci\363n del sistema cl\341sico utilizando LiDAR}{section.150}% 51
\BOOKMARK [2][-]{subsection.178}{5.2.3 Implementaci\363n del sistema basado en Deep Learning utilizando LiDAR}{section.150}% 52
\BOOKMARK [1][-]{section.190}{5.3 Fusi\363n sensorial}{chapter.129}% 53
\BOOKMARK [1][-]{section.196}{5.4 Veh\355culo del proyecto Techs4AgeCar}{chapter.129}% 54
\BOOKMARK [1][-]{section.199}{5.5 Implementaci\363n sobre el veh\355culo Techs4AgeCar}{chapter.129}% 55
\BOOKMARK [0][-]{chapter.201}{6 AD DevKit}{}% 56
\BOOKMARK [1][-]{section.202}{6.1 Motivaci\363n para la creaci\363n del AD DevKit}{chapter.201}% 57
\BOOKMARK [1][-]{section.205}{6.2 Obtenci\363n del groundtruth}{chapter.201}% 58
\BOOKMARK [2][-]{subsection.207}{6.2.1 Calculo de la visibilidad de los objetos}{section.205}% 59
\BOOKMARK [2][-]{subsection.223}{6.2.2 Geometr\355a de la c\341mara}{section.205}% 60
\BOOKMARK [2][-]{subsection.225}{6.2.3 Formato del groundtruth obtenido}{section.205}% 61
\BOOKMARK [2][-]{subsection.228}{6.2.4 Funcionamiento del generador de groundtruth}{section.205}% 62
\BOOKMARK [1][-]{section.231}{6.3 Evaluaci\363n de los modelos}{chapter.201}% 63
\BOOKMARK [0][-]{chapter.234}{7 Resultados obtenidos}{}% 64
\BOOKMARK [1][-]{section.235}{7.1 An\341lisis sobre datasets}{chapter.234}% 65
\BOOKMARK [2][-]{subsection.236}{7.1.1 An\341lisis cuantitativo en Kitti}{section.235}% 66
\BOOKMARK [2][-]{subsection.242}{7.1.2 An\341lisis cuantitativo en nuScenes}{section.235}% 67
\BOOKMARK [2][-]{subsection.247}{7.1.3 An\341lisis adicionales}{section.235}% 68
\BOOKMARK [3][-]{subsubsection.248}{7.1.3.1 Transferencia de modelos basados en Kitti a nuScenes}{subsection.247}% 69
\BOOKMARK [3][-]{subsubsection.252}{7.1.3.2 N\372mero de nubes de puntos de entrada en modelos evaluados sobre nuScenes}{subsection.247}% 70
\BOOKMARK [3][-]{subsubsection.256}{7.1.3.3 Tama\361o del v\363xel en modelos basados en redes neuronales}{subsection.247}% 71
\BOOKMARK [1][-]{section.258}{7.2 An\341lisis sobre el simulador CARLA}{chapter.234}% 72
\BOOKMARK [2][-]{subsection.260}{7.2.1 An\341lisis cualitativo del modelo cl\341sico en CARLA}{section.258}% 73
\BOOKMARK [2][-]{subsection.262}{7.2.2 An\341lisis cualitativo de Pointpillars Multihead en CARLA}{section.258}% 74
\BOOKMARK [3][-]{subsubsection.264}{7.2.2.1 Comparativa con PointPillars en CARLA}{subsection.262}% 75
\BOOKMARK [2][-]{subsection.266}{7.2.3 An\341lisis cuantitativo de Pointpillars Multihead en CARLA}{section.258}% 76
\BOOKMARK [1][-]{section.269}{7.3 An\341lisis sobre el veh\355culo T4AC}{chapter.234}% 77
\BOOKMARK [2][-]{subsection.270}{7.3.1 An\341lisis cualitativo de Pointpillars Multihead sobre el veh\355culo T4AC}{section.269}% 78
\BOOKMARK [3][-]{subsubsection.272}{7.3.1.1 Comparativa con PointPillars sobre el veh\355culo T4AC}{subsection.270}% 79
\BOOKMARK [2][-]{subsection.274}{7.3.2 An\341lisis cualitativo del sistema de fusi\363n sensorial sobre el veh\355culo T4AC}{section.269}% 80
\BOOKMARK [0][-]{chapter.276}{8 Conclusiones y trabajos futuros}{}% 81
\BOOKMARK [1][-]{section.277}{8.1 Conclusiones}{chapter.276}% 82
\BOOKMARK [1][-]{section.278}{8.2 Trabajos futuros}{chapter.276}% 83
\BOOKMARK [0][-]{chapter*.279}{Bibliograf\355a}{}% 84
\BOOKMARK [0][-]{appendix.280}{A Manual de usuario}{}% 85
\BOOKMARK [1][-]{section.281}{A.1 Sistema de detecciones basado en t\351cnicas cl\341sicas}{appendix.280}% 86
\BOOKMARK [1][-]{section.291}{A.2 Muestra de nubes de puntos en KITTI}{appendix.280}% 87
\BOOKMARK [1][-]{section.297}{A.3 Modelo de detecciones basado en Deep Learning}{appendix.280}% 88
\BOOKMARK [1][-]{section.304}{A.4 Sistema de detecciones mediante fusi\363n sensorial}{appendix.280}% 89
\BOOKMARK [1][-]{section.318}{A.5 Uso del AD Devkit para la evaluaci\363n de la capa de percepci\363n}{appendix.280}% 90
\BOOKMARK [0][-]{appendix.366}{B Pliego de condiciones}{}% 91
\BOOKMARK [1][-]{section.367}{B.1 Recursos hardware}{appendix.366}% 92
\BOOKMARK [1][-]{section.368}{B.2 Recursos software}{appendix.366}% 93
\BOOKMARK [0][-]{appendix.369}{C Presupuesto}{}% 94
\BOOKMARK [1][-]{section.370}{C.1 Coste de hardware y software}{appendix.369}% 95
\BOOKMARK [1][-]{section.372}{C.2 Coste de personal}{appendix.369}% 96
\BOOKMARK [1][-]{section.374}{C.3 Coste total}{appendix.369}% 97