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

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 dro...

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Bibliographic Details
Main Authors: Qiao, Zhongzheng, Pham, Xuan Huy, Ramasamy, Savitha, Jiang, Xudong, Kayacan, Erdal, Sarabakha, Andriy
Format: Conference Proceeding
Language:English
Subjects:
aerial robotics
continual learning
Continuing education
Lighting
Logic gates
machine perception
Neural networks
Real-time systems
Robot kinematics
Robustness
Online Access:Request full text
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Summary: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.
ISSN:2161-4407
DOI:10.1109/IJCNN60899.2024.10649903