INDOOR 3D MODELING AND FLEXIBLE SPACE SUBDIVISION FROM POINT CLOUDS

Indoor navigation can be a tedious process in a complex and unknown environment. It gets more critical when the first responders try to intervene in a big building after a disaster has occurred. For such cases, an accurate map of the building is among the best supports possible. Unfortunately, such a map is not always available, or generally outdated and imprecise, leading to error prone decisions. Thanks to advances in the laser scanning, accurate 3D maps can be built in relatively small amount of time using all sort of laser scanners (stationary, mobile, drone), although the information they provide is generally an unstructured point cloud. While most of the existing approaches try to extensively process the point cloud in order to produce an accurate architectural model of the scanned building, similar to a Building Information Model (BIM), we have adopted a space-focused approach. This paper presents our framework that starts from point-clouds of complex indoor environments, performs advanced processes to identify the 3D structures critical to navigation and path planning, and provides fine-grained navigation networks that account for obstacles and spatial accessibility of the navigating agents. The method involves generating a volumetric-wall vector model from the point cloud, identifying the obstacles and extracting the navigable 3D spaces. Our work contributes a new approach for space subdivision without the need of using laser scanner positions or viewpoints. Unlike 2D cell decomposition or a binary space partitioning, this work introduces a space enclosure method to deal with 3D space extraction and non-Manhattan World architecture. The results show more than 90% of spaces are correctly extracted. The approach is tested on several real buildings and relies on the latest advances in indoor navigation

Topology Extraction from Occupancy Grids

A fundamental problem in indoor location-based services is to compute the meaning of location with respect to an indoor location model. One specific challenge in this area is represented by the central tradeoff between two philosophies: a decent amount of the community tries to provide high-quality, high-fidelity models investing specialized knowledge and a lot of time in building such models for each building thereby increasing simplicity and quality of location-based services such as navigation or guidance. In contrast to that, other people argue that crowd sourcing and very simple representations of environmental information are the only way of generating indoor environmental information at scale. However, applications then have to tolerate errors and deal with oversimplified models. With this paper, we show for a specific widely accepted simple environmental model in which building floorplans are represented as black-and-white bitmaps, how we can provide algorithms for extracting higher order topological concepts from these trivial maps. We further illustrate how these can be applied to the hard problem of indoor shortest path calculation, indoor alternative path calculation, indoor spatial statistics, and path segmentation