Robust Perception for Formula Student Driverless Racing
(2021)Department of Automatic Control
- Abstract
- Building an autonomous system for race cars requires robust and highly accurate perception running in real time. This thesis proposes a novel ground removal strategy for 3D LiDAR perception, modelling the ground as several planes, and a novel clustering method for LiDARs that sweep the scene in a predefined pattern resulting in a 20-fold performance increase over clustering methods commonly used for this problem.
To weed out spurious information produced by relying only on LiDAR perception, two sensor fusion methods using a camera are introduced and evaluated, one using the immensely popular YOLO network. All algorithms were evaluated in real world scenarios using a fully functioning Formula Student driverless vehicle built by the Lund... (More) - Building an autonomous system for race cars requires robust and highly accurate perception running in real time. This thesis proposes a novel ground removal strategy for 3D LiDAR perception, modelling the ground as several planes, and a novel clustering method for LiDARs that sweep the scene in a predefined pattern resulting in a 20-fold performance increase over clustering methods commonly used for this problem.
To weed out spurious information produced by relying only on LiDAR perception, two sensor fusion methods using a camera are introduced and evaluated, one using the immensely popular YOLO network. All algorithms were evaluated in real world scenarios using a fully functioning Formula Student driverless vehicle built by the Lund Formula Student 2021 team. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9069372
- author
- Broström, Gustaf and Carpenfelt, David
- supervisor
- organization
- year
- 2021
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6152
- other publication id
- 0280-5316
- language
- English
- id
- 9069372
- date added to LUP
- 2021-12-23 14:19:19
- date last changed
- 2021-12-23 14:19:19
@misc{9069372,
abstract = {{Building an autonomous system for race cars requires robust and highly accurate perception running in real time. This thesis proposes a novel ground removal strategy for 3D LiDAR perception, modelling the ground as several planes, and a novel clustering method for LiDARs that sweep the scene in a predefined pattern resulting in a 20-fold performance increase over clustering methods commonly used for this problem.
To weed out spurious information produced by relying only on LiDAR perception, two sensor fusion methods using a camera are introduced and evaluated, one using the immensely popular YOLO network. All algorithms were evaluated in real world scenarios using a fully functioning Formula Student driverless vehicle built by the Lund Formula Student 2021 team.}},
author = {{Broström, Gustaf and Carpenfelt, David}},
language = {{eng}},
note = {{Student Paper}},
title = {{Robust Perception for Formula Student Driverless Racing}},
year = {{2021}},
}