Mapping Repetitive Structural Tunnel Environments for a Biologically Inspired Climbing Robot

World Scientific
Publication Type:
Conference Proceeding
Assistive Robots: Proceedings of the 18th International Conference on CLAWAR 2015, 2016, pp. 325 - 333
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This paper presents an approach to using noisy and incomplete depth-camera datasets to detect reliable surface features for use in map construction for a caterpillar-inspired climbing robot. The approach uses a combination of plane extraction, clustering and template matching techniques to infer from the restricted dataset a usable map. This approach has been tested in both laboratory and real-world steel bridge tunnel datasets generated by a climbing robot, with the results showing that the generated maps are accurate enough for use in localisation and step trajectory planning.
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