Improvement in the UAV position estimation with low-cost GPS, INS and vision-based system: Application to a quadrotor UAV

TL;DR

This paper presents a novel UAV position estimation system combining low-cost GPS, INS, and vision-based sensors, significantly improving accuracy over traditional GPS-INS fusion methods in outdoor drone operations.

Contribution

The study introduces an integrated position estimation approach using affordable hardware and dense optical flow, enhancing UAV navigation accuracy in real-world outdoor environments.

Findings

Improved position accuracy in outdoor UAV flights.

Effective fusion of GPS, INS, and vision sensors.

Outperforms conventional GPS-INS fusion methods.

Abstract

In this paper, we develop a position estimation system for Unmanned Aerial Vehicles formed by hardware and software. It is based on low-cost devices: GPS, commercial autopilot sensors and dense optical flow algorithm implemented in an onboard microcomputer. Comparative tests were conducted using our approach and the conventional one, where only fusion of GPS and inertial sensors are used. Experiments were conducted using a quadrotor in two flying modes: hovering and trajectory tracking in outdoor environments. Results demonstrate the effectiveness of the proposed approach in comparison with the conventional approaches presented in the vast majority of commercial drones.