Barrier and guardrail extraction and classification from point clouds
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/1893
EDITED VERSION: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B5-2020/157/2020/
DOCUMENT TYPE: article
In the recent years, the modelling of infrastructures has been receiving increasingly attention due to the importance of transport infrastructures for global economy, traffic safety and for the generation of high definition maps, essential to autonomous vehicles. This paper presents a simple method for the segmentation and classification of concrete barriers and guardrails in road surroundings. First steps of the method are aimed to delimit the region of the point cloud outside the driving lanes in which barriers and guardrails are installed. The purpose is to significantly reduce the size of point clouds in order to improve further processing. Then, barrier segmentation and classification are designed as parameter-dependent processes because the geometric features of roads and barriers and guardrails are mostly regulated by norms and standards. Results show a good performance in terms of classification in comparison of other state of the art methods. Better results were obtained for guardrails than for concrete barriers. The method has been tested in a set of point clouds acquired with a Mobile Laser Scanner from conventional roads and highways.
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