I2-S2: Intra-image-SeqSLAM for more accurate vision-based localisation in underground mines

Many real-world robotic and autonomous vehicle applications, such as autonomous mining vehicles, require robust localisation under challenging environmental conditions. Laser range sensors have been used traditionally, but of- ten get lost in long tunnels that are the major components of underground mines. Recent re- search and applied systems have been increasingly using cameras, bringing in new challenges with regards to robustness against appearance and viewpoint changes. In this paper we develop a novel visual place recognition algorithm for autonomous underground mining vehicles that can be used to provide sufficiently accurate (sub-metre) metric pose estimation while also having the appearance-invariant and computationally lightweight characteristics of topological appearance-based methods. The challenge of large viewing angle variations typical in confined tunnels is addressed by incorporating multiple reference image candidates. The framework is evaluated with real-world multi- traverse datasets featuring different environments including underground mining tunnels and office building environments. The reprojection error of image registration is ∼ 50% lower than a state-of-the-art deep-learning based method (MR-FLOW) using manually-labelled ground truth on a set of images representing typical scenarios during the underground mining process.