Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometrically degraded environments. Statistical tests robustify these estimators against radar outliers. However, available work with binary outlier filters lacks adaptability to various hardware setups and environments. Other work has predominantly been tested in handheld static environments or automotive contexts. This work introduces a robust baro-radar-inertial odometry (BRIO) m-estimator for quadcopter flights in typical GNSS-denied scenarios. Extensive real-world closed-loop flights in cities and forests demonstrate robustness to moving objects and ghost targets, maintaining a consistent performance with 0.5 % to 3.2 % drift per distance traveled. Benchmarks on public datasets validate the system's generalizability. The code, dataset, and video are available at https://github.com/ethz-asl/rio.

翻译：搜救任务要求移动机器人能够在非结构化的室内外环境中自主导航。特别是对于主动稳定的多旋翼无人机而言，精确的运动数据对其保持平衡与规避障碍至关重要。将芯片级雷达获取的径向速度与MEMS惯性传感数据相融合，已被证明能够提供鲁棒、轻量且一致的状态估计，即使在视觉或几何特征退化的环境中亦是如此。统计检验方法可增强此类估计器对雷达异常值的鲁棒性。然而，现有采用二元异常值滤波器的工作缺乏对不同硬件配置及环境条件的适应性。其他研究主要集中于手持静态环境或汽车应用场景的测试。本研究提出了一种鲁棒的气压-雷达-惯性里程计（BRIO）M估计器，专为四旋翼飞行器在典型GNSS拒止场景下的飞行而设计。通过在城市与森林环境中进行的大量真实世界闭环飞行实验表明，该系统对运动物体与虚假目标具有强鲁棒性，能够保持每行进距离0.5%至3.2%的漂移率，性能表现稳定。在公开数据集上的基准测试验证了该系统的泛化能力。相关代码、数据集及演示视频可在https://github.com/ethz-asl/rio获取。