Using Algorithm Selection for Adaptive Vehicle Perception Aboard UAV

Christian Hellert, S. Koch, P. Stütz
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引用次数: 9

Abstract

Surveillance sensors aboard UAV are affected by environmental influences, e.g. atmospheric or topographic factors. This paper proposes a method for the automatic adaption of airborne sensor applications such as street surveillance to changing environmental conditions, preventing overall performance degradation with minimum human intervention. The basic principle of the concept relies on the selection of the most appropriate data processing algorithm available on board. To facilitate the determination of the most effective algorithm, performance models are used to predict the expected suitability of each algorithm for the given environmental conditions. Modeling the relation between the environmental state and the performance of the algorithms is achieved by two approaches leveraging expert knowledge and machine learning methods. An evaluation was carried out in simulation as well as in real flight experiments showing that the proposed method is able to improve overall vehicle perception performance.
基于算法选择的无人机自适应车辆感知
无人机上的监视传感器受到环境因素的影响,例如大气或地形因素。本文提出了一种方法,使机载传感器应用(如街道监控)自动适应不断变化的环境条件,以最小的人为干预防止整体性能下降。该概念的基本原理依赖于选择船上可用的最合适的数据处理算法。为了便于确定最有效的算法,使用性能模型来预测每种算法对给定环境条件的预期适用性。通过利用专家知识和机器学习两种方法来建模环境状态与算法性能之间的关系。仿真和实际飞行试验结果表明,该方法能够提高飞行器的整体感知性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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