Multi-State Tightly-Coupled EKF-Based Radar-Inertial Odometry With Persistent Landmarks

This paper presents a Radar-Inertial Odometry (RIO) method that leverages advanced techniques from vision research to accurately estimate a robot’s position and velocity using radar data. The approach combines past robot poses, radar measurements, and Inertial Measurement Unit (IMU) readings in an Extended Kalman Filter (EKF) framework. This method is particularly valuable for Unmanned Aerial Vehicles (UAVs) operating in challenging environments without GNSS, and demonstrates its effectiveness in real flight experiments.