Скачать или смотреть TIMTAM: Tunnel-Image Texturally-Accorded Mosaic for Localization

IEEE Robotics and Automation Letters Paper / also presented at IROS2019:

"TIMTAM: Tunnel-Image Texturally-Accorded Mosaic for Location Refinement of Underground Vehicles with a Single Camera”, Fan Zeng, Adam Jacobson, David Smith, Nigel Boswell, Thierry Peynot, Michael J Milford

Collaborators & funding support: Queensland Government, Caterpillar, Mining3 and QUT

Abstract:

Many mine-site processes such as vehicle operation require localisation systems that are reliable, robust and work in a range of environmental conditions. In underground operations, GPS is not available: solutions instead rely on static infrastructure or expensive, laser-based solutions with limited operational capability. In this paper we present a new vision-based technique, Tunnel-IMage Texturally-Accorded Mosaic (TIMTAM), for submetre, infrastructure-free localisation in underground mining environments using a single camera. Our approach stitches upward-facing camera images to form planar mosaic maps, using locations generated by the coarse mapping engine based on a small number of manually anchored locations. Localisation is achieved by refining coarse location estimations with a best fit pixel location for the query image within a search neighbourhood in the mosaic map. Our direct pixel-based method is more robust to the challenging illumination and surface-texture environments encountered in underground mine operations than feature-based techniques. Localisation refinement is only triggered when a confidence threshold for the estimate is exceeded. The system is evaluated in a real world mine tunnel, with results showing that the confidence threshold approach is predictive of the quality of the location estimate refinement, and achieves a reduction in mean localisation metric error of up to ~66% from simulated coarse results.