ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 287-293, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-287-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
POINT CLOUDS TO INDOOR/OUTDOOR ACCESSIBILITY DIAGNOSIS
J. Balado1, L. Díaz-Vilariño1,2, P. Arias1, and I. Garrido1 1Applied Geotechnologies Group, Dept. Natural Resources and Environmental Engineering, University of Vigo, Campus Lagoas-Marcosende, CP 36310 Vigo, Spain
2GIS Technology, OTB Research Institute for the Built Environment, Julianalaan 134, 2628 BL Delft, the Netherlands
Keywords: Building accessibility, Indoor/Outdoor seamless modelling, indoor/outdoor mobility, 3D reconstruction, Building Information Modelling Abstract. This work presents an approach to automatically detect structural floor elements such as steps or ramps in the immediate environment of buildings, elements that may affect the accessibility to buildings. The methodology is based on Mobile Laser Scanner (MLS) point cloud and trajectory information. First, the street is segmented in stretches along the trajectory of the MLS to work in regular spaces. Next, the lower region of each stretch (the ground zone) is selected as the ROI and normal, curvature and tilt are calculated for each point. With this information, points in the ROI are classified in horizontal, inclined or vertical. Points are refined and grouped in structural elements using raster process and connected components in different phases for each type of previously classified points. At last, the trajectory data is used to distinguish between road and sidewalks. Adjacency information is used to classify structural elements in steps, ramps, curbs and curb-ramps. The methodology is tested in a real case study, consisting of 100 m of an urban street. Ground elements are correctly classified in an acceptable computation time. Steps and ramps also are exported to GIS software to enrich building models from Open Street Map with information about accessible/inaccessible entrances and their locations.
Conference paper (PDF, 1845 KB)


Citation: Balado, J., Díaz-Vilariño, L., Arias, P., and Garrido, I.: POINT CLOUDS TO INDOOR/OUTDOOR ACCESSIBILITY DIAGNOSIS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 287-293, https://doi.org/10.5194/isprs-annals-IV-2-W4-287-2017, 2017.

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