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Interpretation of environments by incremental learning

通过增量学习解释环境

基本信息

批准号：
200550554
负责人：
Professor Dr. Jürgen Gall
金额：
--
依托单位：
Lehrstuhl für Geoinformation
依托单位国家：
德国
项目类别：
Research Units
财政年份：
2011
资助国家：
德国
起止时间：
2010-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/200550554?language=en
关键词：
Interpretation environments incremental learning

项目摘要

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.

语义信息对于自主飞行器捕捉和识别不可接近的物体是必不可少的。例如，它有助于有远见地飞行，并通过识别物体和预测它们的运动来避免碰撞。它通过对难以重建的表面进行分类，改进了环境的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，以指导飞行器的导航和探索行为。例如，要检查一座建筑的所有窗户，不需要重建整个建筑，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。项目P7旨在通过机器学习的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的运行时间和复杂度有不同的要求，因此将开发一个多层系统。在最低层对图像数据进行实时分类，在最高层对环境中的类实例的位置进行概率预测，特别是对于尚未探索的区域。由于该项目第二阶段的一个关键方面是对从以前的飞行重建的地图进行增量更新，因此P7主要侧重于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改变了训练数据的外观。此外，为了解决复杂任务和增强语义查询的灵活性，不会假设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的目标不仅是开发可以在已知类别的新训练样本可用时立即高效更新的分类器，而且还要高效地学习新类别。