  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.
