Embedded Bare-Metal Radar-Inertial Odometry

2026-10-05

AI Summary

This study addresses the high computational overhead and insufficient robustness of conventional vision- and LiDAR-based approaches for resource-constrained platforms operating in perception-degraded environments. To this end, it proposes an embedded radar-inertial odometry system that, for the first time, deploys the full stack of FMCW radar driving, data processing, and aided inertial navigation on a single-core microcontroller. By exploiting the radar’s direct velocity measurement and interference resilience, the method achieves efficient state estimation under severe computational constraints and integrates with the PX4 flight control stack to enable closed-loop flight control. Real-world experiments demonstrate absolute translational trajectory errors of 0.51–0.82 meters and relative pose errors below 3%, validating the feasibility of lightweight autonomous navigation in extreme environments.

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.

Problem

• State Estimation
• Radar-Inertial Odometry
• Resource-Constrained Platforms
• Embedded Systems
• Perceptual Degradation

Innovation

• Radar-Inertial Odometry
• Embedded Systems
• FMCW Radar
• Bare-Metal Microcontroller
• State Estimation