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

TY - GEN

T1 - Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing

AU - Qiao, Zhongzheng

AU - Pham, Xuan Huy

AU - Ramasamy, Savitha

AU - Jiang, Xudong

AU - Kayacan, Erdal

AU - Sarabakha, Andriy

PY - 2024/5/2

Y1 - 2024/5/2

N2 - 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.

AB - 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.

KW - aerial robotics

KW - continual learning

KW - machine perception

UR - http://www.scopus.com/inward/record.url?scp=85204999350&partnerID=8YFLogxK

U2 - 10.1109/ijcnn60899.2024.10649903

DO - 10.1109/ijcnn60899.2024.10649903

M3 - Article in proceedings

BT - 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings

PB - IEEE

T2 - 2024 International Joint Conference on Neural Networks, IJCNN 2024

Y2 - 30 June 2024 through 5 July 2024

ER -