Abstract
In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation. <br><br> Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored. <br><br> In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.
Highlights
It is estimated that people living in cities spend on the average 90% of their time indoors (EPA 2009)
The navigational assistance for blind or wheelchair people is one of the direct applications of indoor path planning, where algorithms are developed to generate efficient routes according to their mobility restrictions (Swobodzinski and Raubal, 2008)
The value and originality of this work consist on considering point clouds for reconstructing semantically-rich 3D indoor models, and for detecting potential obstacles in the route planning and using it for readapting routes according to the real state depictured by the laser scanner
Summary
It is estimated that people living in cities spend on the average 90% of their time indoors (EPA 2009). The navigational assistance for blind or wheelchair people is one of the direct applications of indoor path planning, where algorithms are developed to generate efficient routes according to their mobility restrictions (Swobodzinski and Raubal, 2008). Obstacles detection is fundamental for real indoor path-finding, state of the art researchers and implementation of the indoor navigation do not usually deal with the obstacle issue and routing algorithms mostly consider empty spaces (Liu and Zlatanova, 2013). The value and originality of this work consist on considering point clouds for reconstructing semantically-rich 3D indoor models, and for detecting potential obstacles in the route planning and using it for readapting routes according to the real state depictured by the laser scanner.
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