Indoor UAV Height Estimation with Multiple Model-Detecting Particle Filters

Abstract

The precision of indoor localization, especially height estimation, is critical to unmanned aerial vehicle (UAV) navigation to avoid crashes because indoor environments are narrow and complex. The lack of satellite-based navigation signals makes this task very challenging. Moreover, objects in indoor environments could be randomly shaped and in motion, making map-based navigation unreliable. There exist solutions utilizing advanced sensor arrays such as laser scanners or multiple cameras, but the UAVs' weight load and computational resources are limited. In this paper, we propose a filtering-based method that allows for estimation of the height of the UAV by stand -alone range finders. Model-detecting particle filters are used to detect changes in objects while estimating the height of the UAV simultaneously. Multiple filters are utilized to speed up the computation. Numerical experiments show that the proposed method is more accurate than other methods.