Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

TL;DR

This paper introduces an unsupervised spectral clustering framework for recognizing subterranean tunnel junctions from 2D point clouds, aiding navigation and mapping in underground environments.

Contribution

It presents a novel unsupervised spectral clustering approach specifically designed for tunnel junction detection in subterranean 2D point cloud data.

Findings

Effective in real underground environments

Validated with multiple datasets and simulations

Improves navigation accuracy in subterranean missions

Abstract

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.