Visual Inertial Odometry (VIO) is commonly used for real-time Unmanned Aerial Vehicle (UAV) localization. However, the performance of VIO significantly deteriorates when UAV encounters sudden lighting variation in the environment, which poses a significant risk during flight. To address this issue without introducing additional sensors, a learning-based dead-reckoning algorithm relying solely on inertial measurement, which shares the same source with VIO, is proposed. The core idea of our method tightly couples a model-based Left Invariant Extended Kalman Filter (LIEKF) with a statistical neural network, both driven by raw inertial measurement. We have validated our algorithm for comparable accuracy with commonly deployed VIO methods under favorable lighting conditions and outperforms other IMU dead-reckoning algorithms in open-source datasets and real-world scenarios. To further enhance localization robustness while UAV traverses environments with different lighting conditions, we introduce an approach that tightly integrates our algorithm with VIO, and validate its effectiveness in real-world scenarios. It is believed that our work presents a promising way for enhancing robustness in vision-based localization methods within the robotics society.

中文翻译：

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增强光照突然变化下 VIO 的鲁棒性：用于无人机定位的基于学习的 IMU 航位推算

视觉惯性里程计 (VIO) 通常用于实时无人机 (UAV) 定位。然而，当无人机遇到环境中突然的光照变化时，VIO的性能会显着恶化，这在飞行过程中带来很大的风险。为了在不引入额外传感器的情况下解决这个问题，提出了一种仅依靠惯性测量的基于学习的航位推算算法，该算法与 VIO 具有相同的来源。我们方法的核心思想将基于模型的左不变扩展卡尔曼滤波器（LIEKF）与统计神经网络紧密耦合，两者均由原始惯性测量驱动。我们已经验证了我们的算法在有利的光照条件下与常用部署的 VIO 方法具有相当的精度，并且在开源数据集和现实场景中优于其他 IMU 航位推算算法。为了进一步增强无人机在不同光照条件下穿越环境时的定位鲁棒性，我们引入了一种将我们的算法与 VIO 紧密集成的方法，并在现实场景中验证其有效性。人们相信，我们的工作为增强机器人社会中基于视觉的定位方法的鲁棒性提供了一种有前途的方法。