Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing
This addresses the challenge of resilient real-time perception for autonomous drones in racing, though it appears incremental as it builds on existing neural network methods.
The paper tackles the problem of detecting drone racing gates under dynamic lighting conditions by introducing a lightweight neural network with continual learning, achieving notable robustness in diverse scenarios.
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.