Autonomous Robust Navigation System for MAV Based on Monocular Cameras

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 MAV based on vSLAM where a metric scale can be estimated using a Kalman Filter (KF), based on odometry information. 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.