Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing

Zhongzheng Qiao, Xuan Huy Pham, Savitha Ramasamy, Xudong Jiang, Erdal Kayacan, Andriy Sarabakha

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.
Original languageEnglish
Title of host publicationIJCNN 2024 Conference Proceedings
Number of pages8
PublisherIEEE
Publication date2 May 2024
ISBN (Electronic) 978-8-3503-5931-2
DOIs
Publication statusPublished - 2 May 2024
Event2024 International Joint Conference on Neural Networks, IJCNN 2024 - Yokohama, Japan
Duration: 30 Jun 20245 Jul 2024

Conference

Conference2024 International Joint Conference on Neural Networks, IJCNN 2024
Country/TerritoryJapan
CityYokohama
Period30/06/202405/07/2024

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