解决地下环境雷达惯性里程计受陀螺仪漂移影响的问题
How IMU Drift Influences Multi-Radar Inertial Odometry for Ground Robots in Subterranean Terrains
- 分两阶段设计，用最小二乘法估计雷达自车速度并在线校正陀螺仪偏置
- 在烟尘环境下仍保持稳定定位，优于传统EKF-RIO，支持雷达单模态建图
- 适配低成本与高精度陀螺仪，代码开源，适合地下机器人开发者
可靠雷达惯性里程计（RIO）需抑制陀螺仪偏置漂移，这在地下环境中尤为严峻，因极端温差和重力加速度加剧漂移。成本较低的Pixhawk IMU搭配FMCW TI IWR6843AOP EVM雷达时，因雷达回波稀疏、噪声大且闪烁，融合稳定性低于激光雷达方案。然而，激光雷达在烟雾、粉尘和气溶胶中失效，而FMCW雷达具备紧凑、轻量、低成本且抗干扰优势。为此，提出双阶段多雷达惯性里程计（MRIO）框架：首先通过最小二乘法构建雷达自车速度估计，融入扩展卡尔曼滤波器实现在线陀螺仪偏置校正；再将校正后的加速度与多雷达及IMU异构测量融合，优化里程计。该框架还利用机器人估算的平移与旋转位移，支持纯雷达建图。地下实地测试表明，MRIO在无卫星信号条件下表现稳健，显著优于EKF-RIO，对Pixhawk及VectorNav等不同精度的IMU均具鲁棒性。代码将开源（见https://github.com/LTU-RAI/MRIO）。
原文摘要 · Abstract (English)
Reliable radar inertial odometry (RIO) requires mitigating IMU bias drift, a challenge that intensifies in subterranean environments due to extreme temperatures and gravity-induced accelerations. Cost-effective IMUs such as the Pixhawk, when paired with FMCW TI IWR6843AOP EVM radars, suffer from drift-induced degradation compounded by sparse, noisy, and flickering radar returns, making fusion less stable than LiDAR-based odometry. Yet, LiDAR fails under smoke, dust, and aerosols, whereas FMCW radars remain compact, lightweight, cost-effective, and robust in these situations. To address these challenges, we propose a two-stage MRIO framework that combines an IMU bias estimator for resilient localization and mapping in GPS-denied subterranean environments affected by smoke. Radar-based ego-velocity estimation is formulated through a least-squares approach and incorporated into an EKF for online IMU bias correction; the corrected IMU accelerations are fused with heterogeneous measurements from multiple radars and an IMU to refine odometry. The proposed framework further supports radar-only mapping by exploiting the robot's estimated translational and rotational displacements. In subterranean field trials, MRIO delivers robust localization and mapping, outperforming EKF-RIO. It maintains accuracy across cost-efficient FMCW radar setups and different IMUs, showing resilience with Pixhawk and higher-grade units such as VectorNav. The implementation will be provided as an open-source resource to the community (code available at https://github.com/LTU-RAI/MRIO
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