RIO-SAR: Synthetic Aperture Radar Imaging of Indoor Scenes Based on Radar-Inertial Odometry Using a Mobile Robot

Abstract

Synthetic aperture radar (SAR) imaging provides a method for increasing the resolution of small and low-cost frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar sensors. Using SAR images as an alternative to traditional point cloud-based representations of the environment may improve the performance of simultaneous localization and mapping (SLAM) algorithms for mobile robots. This article presents the details of an indoor mobile robot system that fuses inertial measurement unit (IMU) measurements and radar velocity estimates from an incoherent network of automotive radar sensors using an error-state Kalman filter (ESKF). This trajectory estimate is used to create surround-view SAR images of the robot’s operating environment. The obtained trajectory accuracy is compared against a laboratory reference system, and high-resolution SAR imaging results are presented. The measurement results provide insights into the challenges of robotic millimeter-wave imaging in indoor scenarios.