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Automatic building accessibility diagnosis from point clouds
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DOI：10.1016/j.autcon.2017.06.026.png)
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摘要
En 中文
Building accessibility diagnosis is of high interest especially in case of people with reduced mobility. This paper proposes a methodology for automated detection of inaccessible steps in building fa ade entrances from MLS (mobile laser scanner) data. Our approach uses the MLS trajectory to automatically subdivide urban point clouds into regular stretches. From each stretch, the lower zone of fa ade is isolated and selected as region of interest. Points belonging to vertical elements are projected onto a 2D image and steps are detected and classified as inaccessible areas according to the comparison of geometrical features such as height jump, proximity to ground and width, with regulation. The methodology has been tested in four real datasets, which constitute > 400 m of different urban scenarios. Results exhibit a robust performance under urban scenes with a high variability of fa ade geometry due to the presence of different entrance types to shops and dwellings. Results have been quantitatively evaluated and they show global F1 value around 93%. Moreover, the methodology is very fast since 100 m are processed in < 2 min.
Keyword:
Building accessibility
Building information modelling
Indoor-outdoor seamless
3D data processing
Obstacle detection
Point clouds
Mobile Laser Scanning
3D urban as-built
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