Planetary Exploration via Fully Automatic Topological Structure Extraction using Adaptive Resonance

Jonathan Kissi, University of Western Ontario

Abstract

Renewed interest in Solar System exploration, along with ongoing improvements in computing, robotics and instrumentation technologies, have reinforced the case for remote science acquisition systems development in space exploration. Testing systems and procedures that allow for autonomously collected science has been the focus of analogue field deployments and mission planning for some time, with such systems becoming more relevant as missions increase in complexity and ambition. The introduction of lidar and laser scanning-type instruments into the geological and planetary sciences has proven popular, and, just as with the established image and photogrammetric methods, has found widespread use in several research areas. However, the instrumentation and its data products have attributes, such as extreme accuracy, that can be leveraged in new ways. Success with algorithmic image processing systems such as TextureCam, AEGIS and others lead to the question: can an automated surface characterisation algorithm be created for this new type of data?

We developed a multi-stage, machine learning algorithmic pipeline for this purpose, utilising Adaptive Resonance as a generalised semi-automatic Feature of Interest (FoI) detector. Using data from the RIS⁴E analogue planetary exploration field deployments, we found that this system was highly effective at isolating FoIs at multiple scales, with some direction from an operator. In follow up work, we sought to examine the possibility of fully automating the process. We made additions to the pipeline to facilitate this, by creating a dimensional reduction (DR) based system, that used various metrics to detect a useful classification. We also increased the number and variety of datasets for testing. Finally, we considered the question of dimensionality and what effect the addition of exotic types of point cloud geometry descriptors, such as linearity and curvature, would have on the effectiveness of the classification process. We also added large, natural complex datasets to test the limits of the Adaptive Resonance network. By varying the DR configurations of the pipeline and point descriptors fed to Adaptive Resonance network, we found that tangent normal in combination with UMAP was the most effective, at 72%, for fully automatic FoI detection, from a variety of natural and synthetically generated point cloud datasets.