Experimental Analysis of Quadcopter Drone Hover Constraints for Localization Improvements
Uthman Olawoye, David Akhihiero, Jason N. Gross
TL;DR
This paper addresses improving UAV localization in UAV-UGV cooperation under GNSS-denied conditions by leveraging hover-state information as a dynamic constraint. It extends a multisensor factor graph framework with a unary hover factor, $\\\ ext{\\Psi}_{\\text{hover}}$, that enforces near-zero velocity via a term $\\|v_i\\|_{\\Sigma_{\\text{hover}}}^2$, in combination with LiDAR, UWB ranging, altimeter, and IMU data. Experimental results from a motion-capture facility show that hover constraints can reduce drift and improve accuracy, achieving a 3D RMS as low as ~2.18 m when velocity updates are unavailable, and reducing the maximum error by over 30% (11.57 m to 7.61 m). However, when velocity measurements are present, hover offers limited or slightly detrimental benefits due to potential conflicts in the optimization, highlighting the need for careful uncertainty tuning. The findings inform practical deployment of hover-based constraints as a resilient fallback mechanism in challenging environments.
Abstract
In this work, we evaluate the use of aerial drone hover constraints in a multisensor fusion of ground robot and drone data to improve the localization performance of a drone. In particular, we build upon our prior work on cooperative localization between an aerial drone and ground robot that fuses data from LiDAR, inertial navigation, peer-to-peer ranging, altimeter, and stereo-vision and evaluate the incorporation knowledge from the autopilot regarding when the drone is hovering. This control command data is leveraged to add constraints on the velocity state. Hover constraints can be considered important dynamic model information, such as the exploitation of zero-velocity updates in pedestrian navigation. We analyze the benefits of these constraints using an incremental factor graph optimization. Experimental data collected in a motion capture faculty is used to provide performance insights and assess the benefits of hover constraints.