论文标题
在市区寻找着陆点：多分辨率的概率方法
Finding a Landing Site on an Urban Area: A Multi-Resolution Probabilistic Approach
论文作者
论文摘要
本文考虑了在密集的城市环境中找到无人机的着陆点的问题。快速探索和高分辨率的冲突要求是使用多分辨率方法解决的，该方法由无人机在减少高度下收集视觉信息，以使所获得图像的空间分辨率单调增加。概率分布用于捕获每个地形贴片的决策过程的不确定性。随着收集来自不同高度的信息的信息，将更新分布。当其中一个贴片的置信度高于预先指定的阈值时，宣布着陆的适用性。该方法的主要构建块之一是语义分割算法，该算法将概率附加到单个视图的每个像素上。决策算法将这些概率与先验数据和先前的测量相结合，以获得最佳估计。通过介绍了由现实的闭环模拟器生成的许多示例来说明可行性。
This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirement of fast exploration and high resolution is solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of the patches becomes larger than a pre-specified threshold, suitability for landing is declared. One of the main building blocks of the approach is a semantic segmentation algorithm that attaches probabilities to each pixel of a single view. The decision algorithm combines these probabilities with a priori data and previous measurements to obtain the best estimates. Feasibility is illustrated by presenting a number of examples generated by a realistic closed-loop simulator.
