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.

翻译：在自主移动机器人领域，尤其是在自主无人机竞速这类包含未知动态要素的场景中，实现弹性实时环境感知是一项核心挑战。本研究提出了一种在高速飞行中常见的光照变化条件下检测竞速门的新型感知技术。该技术基于轻量级神经网络主干架构，并融合了持续学习能力。所设想的方法整合了门的位置坐标、距离和方向预测，将其封装为统一的位姿元组。大量测试充分验证了该方法在应对包含动态光照在内的多样化复杂场景时的有效性。所提方法在光照变化场景中展现出显著的鲁棒性，从而证实了其实用价值。