Indoor Navigation From Point Clouds: 3d Modelling and Obstacle Detection

Abstract

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.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.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.

3d Geospatial Indoor Navigation for Disaster Risk Reduction and Response in Urban Environment

“…Point clouds can also be utilized to detect potential obstacles (Vilariño et al, 2016). Such models could be developed as interactive and context-sensitive by utilizing the sensor on the mobile devices which include built-in accelerometer, compass and camera capabilities (Low et al, 2015).…”

Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing

“…While the two first representations are enough for visualization purposes, the reconstruction of semantically-rich representations such as BIM is required for a variety of applications in which, apart from the detailed geometry, semantics and topology play an important role. These applications are mainly routing and navigation [1], crisis management [2], energy efficiency analysis [3], and structural health monitoring, inter alia.…”

Geometrical Network Model Generation Using Point Cloud Data for Indoor Navigation

“…However, when a quadtree and an octree are applied to voxel processing, based on the restriction that indoor space consists of hexahedrons, the input point clouds should be rotated and translated to suit each hexahedron surface to the x, y, and z axes. Moreover, in path planning based on the Voronoi Diagram (Díaz-Vilariño et al 2016), a navigable model from a door to a destination such as a chair is reconstructed in a meeting room. These studies are based on the shortest path-planning approaches between the current location to a destination with obstacle avoidance in a local space.…”