Publicação
3D object detection for autonomous driving using active stereoscopic vision with extended range
| Resumo: | According to the World Health Organization, around 1.19 million people die every year as a result of road accidents caused essentially by human error. For this reason, advanced driver-assistance systems have been developed for several years, with the aim of increasing road safety. In order to perceive the surrounding environment, these systems rely on vision sensors such as cameras, RaDARs and LiDARs. Of all these sensors, LiDAR is the only one that can capture a high-resolution point cloud, enabling 3D object detection. However, a LiDAR is made up of high-precision components, which makes it expensive. One way to circumvent this barrier is to use active stereoscopic vision to build a LiDAR. As a result, the proposed LiDAR consists of affordable and mass-produced components such as cameras and lasers. However, this approach has its challenges, such as noise, correspondence, calibration and centroid estimation. This thesis focuses on noise. We propose an alternative LiDAR sensor based on active stereoscopic vision, featuring a dot projector capable of increasing its range. In this work, the difference in 3D object detection performance between an ideal LiDAR, a conventional active stereo rig and the proposed LiDAR is evaluated using the same set of synthetically generated ideal point clouds. The results obtained show that the extreme simplification assumed when building the ray optics model did not allow to prove that the proposed LiDAR is more accurate than conventional stereoscopic LiDAR when it comes to detecting 3D objects. |
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| Autores principais: | Dias, Renato Alexandre Lourenço |
| Assunto: | Autonomous driving LiDAR Point cloud 3D object detection Stereo vision |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | According to the World Health Organization, around 1.19 million people die every year as a result of road accidents caused essentially by human error. For this reason, advanced driver-assistance systems have been developed for several years, with the aim of increasing road safety. In order to perceive the surrounding environment, these systems rely on vision sensors such as cameras, RaDARs and LiDARs. Of all these sensors, LiDAR is the only one that can capture a high-resolution point cloud, enabling 3D object detection. However, a LiDAR is made up of high-precision components, which makes it expensive. One way to circumvent this barrier is to use active stereoscopic vision to build a LiDAR. As a result, the proposed LiDAR consists of affordable and mass-produced components such as cameras and lasers. However, this approach has its challenges, such as noise, correspondence, calibration and centroid estimation. This thesis focuses on noise. We propose an alternative LiDAR sensor based on active stereoscopic vision, featuring a dot projector capable of increasing its range. In this work, the difference in 3D object detection performance between an ideal LiDAR, a conventional active stereo rig and the proposed LiDAR is evaluated using the same set of synthetically generated ideal point clouds. The results obtained show that the extreme simplification assumed when building the ray optics model did not allow to prove that the proposed LiDAR is more accurate than conventional stereoscopic LiDAR when it comes to detecting 3D objects. |
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