Embedded radar and inertial system improves small vehicle navigation
Embedded Bare-Metal Radar-Inertial Odometry
Robotics
Summary
Small robots and flying drones exploring harsh places need to know where they are, but typical methods using cameras or lasers demand a lot of computing power and fail when the environment looks bad. The authors show that a special type of radar paired with motion sensors can accurately track position and speed without needing much processing power. They designed software and hardware to run this system on a tiny, single-core microcontroller. Tests on flying drones show it can track movements closely and work well with standard autopilot software.
What this means in practice
- •For small rover engineers: Enable compact rovers to estimate position accurately using low-power radar and inertial sensors in challenging, GPS-denied terrain.
- •For drone developers: Integrate radar-inertial odometry on resource-limited drones to improve autonomous flight stability without relying on high-power computing or visual sensors.
Authors
Nicolai Adil Øyen Aatif, Morten Nissov, Kostas Alexis
Abstract
Compact extraplanetary rovers and micro aerial vehicles require robust state estimation frameworks designed to operate under strict computational constraints in unforgiving environments. Typical solutions involving vision- or LiDAR-based sensing are computationally expensive and vulnerable to environments with perceptual degradation, making them poorly suited for resource-constrained platforms and austere conditions. Alternatively, Frequency Modulated Continuous Wave (FMCW) radar offers both robustness and computational efficiency by directly providing velocity measurements coupled with inherent resilience to perceptual degradation. These factors enable robust estimation, thereby reducing reliance on human operators for supervision to ensure platform safety in challenging environments. In this manuscript, we propose an embedded radar-inertial estimator tailored for low-compute platforms. All sensor drivers, data processing, and aided inertial navigation are performed on a single-core microcontroller, demonstrating its computational efficiency. Flight experiments show translational APE of $0.51-0.82\,\mathrm{m}$ and RPE of $<$3\,\% against motion capture, alongside closed-loop flight through an unmodified PX4 stack. The firmware and printed circuit board design are openly available on GitHub at \href{https://github.com/ntnu-arl/embedded_rio}{ntnu-arl/embedded\_rio} and \href{https://github.com/ntnu-arl/embedded_rio-pcb}{ntnu-arl/embedded\_rio-pcb} respectively.