论文标题
基于2D点云的地下交界处识别的无监督学习
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
论文作者
论文摘要
本文提出了一个新颖的无监督学习框架，用于根据获得的2D点云检测地下环境中隧道连接的数量。该框架的实施为高级任务计划者提供了有价值的信息，可以在未知区域或机器人归宿任务中浏览空中平台。该框架利用光谱聚类，该频谱聚类能够从位于非线性歧管上的连接数据点中发现隐藏的结构。光谱聚类算法通过利用从这些点的成对相似性得出的矩阵的特征分解来计算原始2D点云的光谱嵌入。我们使用多个数据集验证了开发的框架，这些数据集从多个逼真的模拟以及地下环境中的实际飞行中收集，证明了所提出的方法的性能和优点。
This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology.
