Caldas, Kenny Anderson Queiroz (University of São Paulo), Benevides, João Roberto (University of São Paulo), Inoue, Roberto Santos (Federal University of São Carlos), Terra, Marco Henrique (University of Sao Paulo at Sao Carlos)

Autonomous Robust Navigation System for MAV Based on Monocular Cameras

Scheduled for presentation during the Regular Session "Navigation" (FrB4), Friday, June 24, 2022, 12:10−12:30, Divona-2

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

Performing autonomous navigation of commercial Micro Air Vehicles (MAVs) in GPS-denied environments without external sensors or a motion capture system is a major challenge in trajectory tracking applications. A convenient solution for this problem is the use of the MAV's odometry, which uses inertial sensors for pose estimation. However, it can suffer from position drifting over time, which may lead to incorrect pose measurements. Another solution is to use the MAV's monocular camera for pose estimation, based on visual Simultaneous Localization and Mapping (vSLAM) algorithms. A limitation of this approach is that monocular vSLAM lacks a metric scale, hence, making its use unfeasible for position feedback. In this scenario, we propose a robust navigation system of a commercial MAV based on vSLAM, where a metric scale can be estimated using a Kalman Filter (KF) based on odometry information with fast convergence. In this work, we opted for the use of a Robust Linear-Quadratic Regulator (R-LQR), which is a recursive strategy that takes into account parametric uncertainties on the system's dynamic. We present simulated and experimental results of our approach using a commercial quadrotor to show the effectiveness of the system.