  The existence of a worldwide indoor floorplans database can lead to
significant growth in location-based applications, especially for indoor
environments. In this paper, we present CrowdInside: a crowdsourcing-based
system for the automatic construction of buildings floorplans. CrowdInside
leverages the smart phones sensors that are ubiquitously available with humans
who use a building to automatically and transparently construct accurate motion
traces. These accurate traces are generated based on a novel technique for
reducing the errors in the inertial motion traces by using the points of
interest in the indoor environment, such as elevators and stairs, for error
resetting. The collected traces are then processed to detect the overall
floorplan shape as well as higher level semantics such as detecting rooms and
corridors shapes along with a variety of points of interest in the environment.
Implementation of the system in two testbeds, using different Android phones,
shows that CrowdInside can detect the points of interest accurately with 0.2%
false positive rate and 1.3% false negative rate. In addition, the proposed
error resetting technique leads to more than 12 times enhancement in the median
distance error compared to the state-of-the-art. Moreover, the detailed
floorplan can be accurately estimated with a a relatively small number of
traces. This number is amortized over the number of users of the building. We
also discuss possible extensions to CrowdInside for inferring even higher level
semantics about the discovered floorplans.
