发表机构
Norwegian University of Science and Technology（挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对紧凑型漫游车和微型飞行器，提出一种基于FMCW雷达和惯性测量单元的单核微控制器嵌入式里程计，实现低计算开销的稳健状态估计，实验验证了其高精度和闭环飞行能力。
AI 中文摘要
紧凑型地外行星漫游车和微型飞行器需要能够在严格计算约束和恶劣环境下稳健运行的状态估计框架。基于视觉或激光雷达的典型解决方案计算成本高，且易受感知退化环境影响，因此不适合资源受限平台和严苛条件。相比之下，调频连续波（FMCW）雷达通过直接提供速度测量并具备对感知退化的固有鲁棒性，兼具鲁棒性和计算效率。这些特性使得稳健估计成为可能，从而减少对人工操作员监督的依赖，以确保平台在挑战性环境中的安全性。本文提出了一种专为低计算平台设计的嵌入式雷达-惯性估计器。所有传感器驱动、数据处理和辅助惯性导航均在单核微控制器上执行，展示了其计算效率。飞行实验表明，相对于运动捕捉系统，平移绝对位姿误差（APE）为0.51-0.82米，相对位姿误差（RPE）小于3%，并实现了通过未修改的PX4软件栈的闭环飞行。固件和印刷电路板设计分别在GitHub上公开：ntnu-arl/embedded_rio和ntnu-arl/embedded_rio-pcb。
英文摘要
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 https://github.com/ntnu-arl/embedded_rio and https://github.com/ntnu-arl/embedded_rio-pcb respectively.
