Al-Radaideh, Amer (New Mexico State University), Sun, Liang (New Mexico State University)

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

Scheduled for presentation during the Regular Session "Sensor Fusion II" (ThC3), Thursday, June 13, 2019, 16:00−16:20, Heritage A

2020 International Conference on Unmanned Aircraft Systems (ICUAS), June 11-14, 2019, Athens, Greece

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

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.