2D点群に基づく地下トンネル分岐点認識のための教師なし学習
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
2D点群から地下トンネルの分岐点の数を検出するスペクトラルクラスタリングを用いた教師なし学習フレームワークを提案し、シミュレーションと実飛行で有効性を示した。
著者: Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano Arranz, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos
分類: cs.RO, cs.CV
原文アブストラクト
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.
