Automatic building accessibility diagnosis from point clouds

Abstract

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 façade 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 façade 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 façade 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.