论文标题
学习在未知环境中即时猛烈抨击：在视觉上模棱两可的无人机的持续学习方法
Learning to SLAM on the Fly in Unknown Environments: A Continual Learning Approach for Drones in Visually Ambiguous Scenes
论文作者
论文摘要
学习在未知环境中安全导航是监视和救援操作中使用的自动无人机的重要任务。近年来，已经提出了许多基于学习的同时定位和映射（SLAM）系统，这些系统依赖于深神经网络（DNNS），该系统已针对传统特征描述符表现不佳的应用。但是，这种基于学习的SLAM系统依靠DNN功能编码在典型的深度学习环境中训练有素的离线训练。这使得它们不太适合在训练期间未见的环境中部署的无人机，在训练中，持续适应至关重要。在本文中，我们提出了一种新的方法，可以通过调节低复杂性词典学习和稀疏编码（DLSC）管道，并使用新提出的二次贝叶斯惊喜（QBS）因素调节，以学习在未知环境中即时猛烈抨击。我们通过在充满挑战的仓库场景中通过无人机收集的数据来实验验证我们的方法，在这种情况下，大量模棱两可的场景使视觉上的歧义很难。
Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-based Simultaneous Localisation and Mapping (SLAM) systems relying on deep neural networks (DNNs) have been proposed for applications where conventional feature descriptors do not perform well. However, such learning-based SLAM systems rely on DNN feature encoders trained offline in typical deep learning settings. This makes them less suited for drones deployed in environments unseen during training, where continual adaptation is paramount. In this paper, we present a new method for learning to SLAM on the fly in unknown environments, by modulating a low-complexity Dictionary Learning and Sparse Coding (DLSC) pipeline with a newly proposed Quadratic Bayesian Surprise (QBS) factor. We experimentally validate our approach with data collected by a drone in a challenging warehouse scenario, where the high number of ambiguous scenes makes visual disambiguation hard.
