Interpretation of environments by incremental learning

语义信息对于自主飞行器捕获和识别不可接近物体至关重要。例如，它有助于通过识别物体并预测它们的运动来预见飞行并避免碰撞。它通过对难以重建的表面进行分类来改进环境的3D重建。然而，首先也是最重要的是，它允许用户进行语义查询，从而引导飞行器的导航和探索行为。例如，为了检查建筑物的所有窗户，不需要重建整个建筑物，这是耗时的。相反，基于环境的当前语义解释来预测所有观察到的和未观察到的窗口的位置就足够了。P7项目旨在通过机器学习方法从增量重建的环境中提取必要的信息。由于从语义冲突避免到语义查询的任务对语义表示有不同的运行时间和复杂性要求，因此将开发一个多层系统。在最低层上，图像数据被实时分类，在最高层上，类实例在环境中的位置被概率地预测，特别是对于尚未被探索的区域。由于该项目第二阶段的一个关键方面是增量更新从以前的飞行重建的地图，P7主要集中在开发有效的增量学习方法。这是具有挑战性的，因为时间和照明条件的变化也会改变训练数据的外观。此外，为了解决复杂的任务和增强语义查询的灵活性，将不假定所有类都是先验已知的。相反，用户可以通过直观的图形用户界面标记当前地图中的区域，并将标记的区域声明为新类的实例。因此，P7的目标不仅是开发能够在已知类别的新训练样本可用时立即有效更新的分类器，而且还可以有效地学习新类别。

Semantic information is essential to capture and identify inaccessible objects by an autonomous flying vehicle. For instance, it helps to fly with foresight und avoid collisions by identifying objects and anticipating their movements. It improves the 3D reconstruction of the environment by classifying surfaces that are difficult to reconstruct. First and foremost, however, it allows semantic queries by a user that steer the navigation and exploration behavior of a flying vehicle. For inspecting all windows of a building for instance, it is not necessary to reconstruct the entire building, which is time-consuming. Instead, it is sufficient to predict the locations of all observed and unobserved windows based on the current semantic interpretation of the environment. Project P7 aims to extract the necessary information from the incrementally reconstructed environments by machine learning approaches. Since the tasks ranging from semantic collision avoidance to semantic queries have different runtime and complexity requirements for the semantic representation, a multi-layer system will be developed. On the lowest layer image data is classified in real-time, on the highest layer the locations of class instances in an environment are probabilistically predicted, in particular, for regions that have not been explored yet. Since a key aspect of the second stage of the project is the incremental update of the map reconstructed from previous flights, P7 mainly focuses on the development of efficient incremental learning approaches. This is challenging since changes of time and lighting conditions also change the appearance of the training data. Furthermore, it will not be assumed that all classes are known a priori in order to solve complex tasks and to enhance the flexibility of semantic queries. Instead, the user can mark regions in the current map via an intuitive graphical user interface and declare the marked regions as instances of a new class. The goal of P7 is therefore not only the development of classifiers that can be efficiently updated as soon as new training samples of known classes are available, but that also learn efficiently new classes.