Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments

TL;DR

The paper tackles robust control and state estimation for quadrotors operating in GPS-denied, non-inertial environments where platform-induced accelerations bias onboard sensors. It introduces an Extended Kalman Filter with Unknown Inputs (EKF-UI) that explicitly models three unknown platform accelerations, coupled with a reduced translational state $z \in \mathbb{R}^9$, and integrates this estimator with a cascaded PID controller for 3D tracking. Experimental validation on a moving-cart setup with dual motion capture confirms that EKF-UI significantly reduces velocity bias and enhances stability and tracking accuracy compared to a standard EKF, particularly under translational and diagonal motions. The approach relies on external position sensing to isolate control performance from perception noise and demonstrates a practical pathway for deploying UAVs on moving platforms such as vehicles or elevators in GPS-denied scenarios.

Abstract

We present a robust control and estimation framework for quadrotors operating in Global Navigation Satellite System(GNSS)-denied, non-inertial environments where inertial sensors such as Inertial Measurement Units (IMUs) become unreliable due to platform-induced accelerations. In such settings, conventional estimators fail to distinguish whether the measured accelerations arise from the quadrotor itself or from the non-inertial platform, leading to drift and control degradation. Unlike conventional approaches that depend heavily on IMU and GNSS, our method relies exclusively on external position measurements combined with a Extended Kalman Filter with Unknown Inputs (EKF-UI) to account for platform motion. The estimator is paired with a cascaded PID controller for full 3D tracking. To isolate estimator performance from localization errors, all tests are conducted using high-precision motion capture systems. Experimental results in a moving-cart testbed validate our approach under both translational in X-axis and Y-axis dissonance. Compared to standard EKF, the proposed method significantly improves stability and trajectory tracking without requiring inertial feedback, enabling practical deployment on moving platforms such as trucks or elevators.

Figures (8)

• Figure 1: Block diagram of the horizontal PID controller for the $x$ and $y$ directions. Desired position is converted into velocity commands and then into body-frame attitude commands.
• Figure 2: Block diagram of the vertical PID controller for the $z$ direction. Altitude error is processed through a cascade to compute the final thrust command.
• Figure 3: System overview of the proposed experimental platform for drone state estimation under non-inertial reference frames. The setup includes dual motion capture systems, a mobile cart, and a Crazyflie 2.0 micro aerial vehicle integrated with ROS-based control.
• Figure 4: Experiment 1 (Stationary Hover Test): Comparison of 3D position estimation. The blue curves correspond to the proposed EKF-UI method, while the red curves denote the baseline standard EKF. The first, second, third row shows the position along the $x$-axis, $y$-axis, and the $z$-axis.
• Figure 5: Experiment 1 (Stationary Hover Test): Comparison of 3D velocity estimation. The blue curves represent the proposed EKF-UI method, whereas the red curves correspond to the baseline standard EKF. The first, second, third row presents the velocity along the $x$-axis, $y$-axis, and the $z$-axis.