Paper Info
Title
Timtam: Tunnel-Image Texturally Accorded Mosaic For Location Refinement Of Underground Vehicles With A Single Camera
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 letter we present a new vision-based technique, Tunnel-IMage Texturally-Accorded Mosaic (TIMTAM), for sub-metre, 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 similar to 66% from simulated coarse results.
Year
DOI
Venue
2019
10.1109/LRA.2019.2932579
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
Field
DocType
Mining robotics, localization, computer vision for automation
Small number,Computer vision,Control engineering,Planar,Global Positioning System,Pixel,Artificial intelligence,Engineering,Underground mining (hard rock)
Journal
Volume
Issue
ISSN
4
4
2377-3766
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Fan Zeng100.34
Adam Jacobson2768.71
David B. Smith334223.45
Nigel Boswell411.02
Thierry Peynot510714.82
Michael Milford6122184.09