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Multicopter Unmanned Aerial Vehicles (UAV) are small and agile robots with the potential to become prominent in performing autonomous tasks in various Global Navigation Satellite System (GNSS)-denied environments. These environments can potentially be rendered even more challenging due to external factors impairing the robot's perception, such as low or too bright light, permeation with aerosols or smoke. A precondition of autonomous operation, though, is the ability of a robot to accurately localize itself in the surrounding environment. Millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar sensors are resilient to the aforementioned factors while being lightweight, inexpensive and highly accurate. In this paper, we present a Radar-Inertial Odometry (RIO) method for estimating the full 6DoF pose and 3D velocity of a UAV. In an Extended Kalman Filter (EKF) framework, we fuse range measurements and velocity measurements of 3D points detected by an FMCW radar sensor together with Inertial Measurement Unit (IMU) readings. In real experiments we show that our approach enables accurate state estimation of a UAV and that it exhibits improvements over similar existing state-of-the-art method.
Michalczyk et al. (Sun,) studied this question.