In this paper we present the Oxford Road Boundaries Dataset, designed for training and testing machine-learning-based road-boundary detection and inference approaches. We have hand-annotated two of the 10 km-long forays from the Oxford Robotcar Dataset and generated from other forays several thousand further examples with semi-annotated road-boundary masks. To boost the number of training samples in this way, we used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions. As a result, we release 62605 labelled samples, of which 47639 samples are curated. Each of these samples contains both raw and classified masks for left and right lenses. Our data contains images from a diverse set of scenarios such as straight roads, parked cars, junctions, etc. Files for download and tools for manipulating the labelled data are available at: oxford-robotics-institute.github.io/road-boundaries-dataset
@article{arxiv.2106.08983,
title = {The Oxford Road Boundaries Dataset},
author = {Tarlan Suleymanov and Matthew Gadd and Daniele De Martini and Paul Newman},
journal= {arXiv preprint arXiv:2106.08983},
year = {2021}
}
Comments
Accepted for publication at the workshop "3D-DLAD: 3D-Deep Learning for Autonomous Driving" (WS15), Intelligent Vehicles Symposium (IV 2021)