Lost in gates: enhancing state estimation in high speed autonomous drone racing

Pinzarrone, Flavio (2023) Lost in gates: enhancing state estimation in high speed autonomous drone racing. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Creative Commons: Attribuzione - Non commerciale - Condividi allo stesso modo 4.0 (CC BY-NC-SA 4.0)

Download (4MB)

Abstract

This thesis delves into the integration of Artificial Intelligence (AI) within the realm of autonomous racing drones. Traditionally, the racing industry has led the way in advancing resilient and streamlined systems, with recent emphasis shifting towards the implementation of autonomous driving mechanisms. The intricacies of this field present complex challenges, demanding the development of precise control and perception systems that operate with minimal reaction times and constrained resources. The research, conducted within the Drone Racing team at the Autonomous Robotics Research Center of the Technology Innovation Institute, primarily focuses on advancing perception and state estimation systems for autonomous racing drones. Central to the study is the introduction of a novel high-speed autonomous drone racing multimodal dataset and an innovative map-based, perceptually aware state estimation technique. This work is instrumental in pushing the boundaries of autonomous drone racing technology, offering valuable insights and solutions that contribute to the broader advancements within the field of autonomous systems.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Pinzarrone, Flavio
Relatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Computer Vision,Autonomous Drone Racing,State Estimation,Robotics,Perception
Data di discussione della Tesi
16 Dicembre 2023
URI

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento

^