自主无人机竞速中动态光照下用于鲁棒门检测的持续学习
Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing
AI总结：
本文针对自主无人机竞速中动态光照导致的门检测困难问题，提出一种结合轻量级神经网络与持续学习的感知方法，通过融合位置、距离和朝向预测实现鲁棒门检测。
摘要：
在自主与移动机器人领域，一个主要挑战是在未知和动态元素的情境下实现具有韧性的实时环境感知，自主无人机竞速场景即为典型例子。本研究提出了一种在光照变化条件下检测无人机竞速门的感知技术，光照变化在高速无人机飞行中十分常见。所提技术依赖于一个轻量级神经网络骨干，并增强了持续学习能力。该方法融合了对门的位置坐标、距离和朝向的预测，将其封装为一个统一的位姿元组。大量测试验证了该方法在应对多样且具有挑战性的场景（尤其是光照条件变化的场景）时的有效性。所提方法在面对光照变化时表现出显著的鲁棒性，从而证明了其有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
