Observability Analysis and Bayesian Filtering for Self-Localization of a Tethered Multicopter in GPS-Denied Environments

A main challenge for multicopter unmanned aerial vehicles (UAVs) is to consistently obtain its accurate position. The integration of the Inertial Navigation System (INS) and Global Positioning System (GPS) is a common strategy to compensate the accumulated drifting errors caused by the onboard Inertial Measurement Unit (IMU). In environments where the GPS signal is degraded or unavailable (e.g., cluttered, hostile, urban, and underwater areas), other solutions must be pursued for the multicopter localization. In this paper, a novel approach is presented that estimates the relative position of a multicopter tethered to a ground mobile platform. The proposed approach uses the measurements collected by solely the commercial-of-the-shelf (COTS) IMU onboard the multicopter. The observability analysis of the system is performed to demonstrate the validity of using a Bayesian filter that was developed to account for the uncertainty in the measurements. Simulation were conducted and the results showed that the developed Bayesian filter, with accurate localization estimates, outperforms a Low-Pass-Filtering approach that was developed by the authors before.