Optical Navigation in Unstructured Dynamic Railroad Environments

TL;DR

This paper introduces a novel optical navigation method for trains operating in unstructured and dynamic environments, enabling train motion estimation solely from track bed observations to support SmartRail 4.0.

Contribution

It presents a new approach for robust train motion estimation using optical data in challenging, occlusion-prone environments, reducing reliance on costly infrastructure.

Findings

Successful validation on real rail scenarios

Robust estimation of translation and rotation achieved

Approach handles occlusions and repetitive track areas

Abstract

We present an approach for optical navigation in unstructured, dynamic railroad environments. We propose a way how to cope with the estimation of the train motion from sole observations of the planar track bed. The occasional significant occlusions during the operation of the train limit the available observation to this difficult to track, repetitive area. This approach is a step towards replacement of the expensive train management infrastructure with local intelligence on the train for SmartRail 4.0. We derive our approach for robust estimation of translation and rotation in this difficult environments and provide experimental validation of the approach on real rail scenarios.