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.

在自主移动机器人中，弹性实时环境感知是一个主要挑战，该研究引入了一种感知技术，可以检测在高速无人机飞行中常见的光照变化情况下的无人机赛门，该方法依靠轻量级神经网络骨干结构，并具备持续学习能力，实验测试表明该方法在各种困难情景下均表现出显著的鲁棒性，特别是在面对光照变化的情况下证明了其有效性。