Applied Aerial Autonomy for Reliable Indoor Flight and 3D Mapping

Abstract

Uncrewed Aerial Systems (UAS) are essential for safely exploring indoor environments damaged by shelling, fire, floods, and structural collapse. These systems can gather critical visual and locational data, aiding in hazard assessment and rescue planning without risking human lives. Reliable UAS deployments requires advanced sensors and robust algorithms for real-time data processing and safe navigation, even in GPS-denied and windy conditions. This dissertation details three research projects to improve UAS performance: (1) in-flight calibration to improve estimation and control, (2) system identification for wind rejection, and (3) indoor aerial 3D mapping.

The dissertation begins with introducing a comprehensive nonlinear filtering framework for UAV parameter estimation, which considers factors such as external wind, drag coefficients, IMU bias, and center of pressure. Additionally, it establishes optimized flight trajectories for parameter estimation through empirical observability. Moreover, an estimation and control framework is implemented, utilizing the mean of state and parameter estimates to generate suitable control inputs for vehicle actuators. By employing a square-root unscented Kalman filter (sq-UKF), this framework can derive a 23-dimensional state vector from 9-dimensional sensor data and 4-dimensional control inputs. Numerical results demonstrate enhanced tracking performance through the integration of the estimation framework with a conventional model-based controller. The estimation of unsteady winds results in improved gust rejection capabilities of the onboard controller as well.

Closely related to parameter estimation is system identification. Combining with the previous work a comprehensive system identification framework with both linear offline and nonlinear online methods is introduced. Inertial parameters are estimated using frequency-domain linear system identification, incorporating control data from motor-speed sensing and state estimates from automated frequency sweep maneuvers. Additionally, drag-force coefficients and external wind are recursively estimated during flight using a sq-UKF. A custom flight controller is developed to manage the computational demands of online estimation and control. Flight experiments demonstrate the tracking performance of the nonlinear controller and its improved capability in rejecting gust disturbances.

Aside from wind rejection, aerial indoor 3D mapping is also required for indoor navigation, and therefore, the dissertation introduces a comprehensive pipeline for real-time mapping and target detection in indoor environments with limited network access. Seeking a best-in-class UAS design, it provides detailed analysis and evaluation of both hardware and software components. Experimental testing across various indoor settings demonstrates the system's efficacy in producing high-quality maps and detecting targets.