论文标题
通过将域图适应深度预测不准确性，无人机的稳健单眼定位
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies
论文作者
论文摘要
我们通过共同训练基于深度学习的深度预测和基于贝叶斯过滤的姿势推理来提出一个新颖的单眼定位框架。所提出的跨模式框架对模型可伸缩性和对环境变化的耐受性极大地优于深度学习的预测。具体而言，我们几乎没有对姿势准确性的降解，即使从轻质深度预测器中的深度估计值极差。与标准深度学习相比，我们的框架在极端照明变化中也保持了高姿势的准确性，即使没有明确的域名适应。通过公开表示地图和中间特征地图（例如深度估计），我们的框架还可以更快地更新并重复使用其他任务（例如避免障碍物）的中间预测，从而提高了资源效率。
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predictions with respect to model scalability and tolerance to environmental variations. Specifically, we show little-to-no degradation of pose accuracy even with extremely poor depth estimates from a lightweight depth predictor. Our framework also maintains high pose accuracy in extreme lighting variations compared to standard deep learning, even without explicit domain adaptation. By openly representing the map and intermediate feature maps (such as depth estimates), our framework also allows for faster updates and reusing intermediate predictions for other tasks, such as obstacle avoidance, resulting in much higher resource efficiency.
