一种用于城市与森林环境中多旋翼飞行器导航的鲁棒气压-雷达-惯性里程计m估计器
A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests
AI总结：
针对现有雷达惯性估计器异常值滤波器适应性不足、测试场景有限的问题，提出适用于无GNSS四旋翼的鲁棒气压-雷达-惯性里程计m估计器，经真实场景与公开数据集验证具备强鲁棒性与泛化性。
摘要：
搜救行动要求移动机器人能在非结构化的室内外环境中导航。尤其是主动稳定的多旋翼无人机，需要精确的运动数据来保持平衡并规避障碍物。将片上雷达的径向速度与MEMS惯性传感相结合，已被证明可提供鲁棒、轻量且一致的状态估计，即便在视觉或几何条件退化的环境中也能工作。统计检验可增强这些估计器对雷达异常值的鲁棒性。但现有采用二值异常值滤波器的研究，缺乏对不同硬件配置与环境的适应性。其他相关研究大多仅在手持静态环境或汽车场景中完成测试。本研究提出了一种鲁棒的气压-雷达-惯性里程计（baro-radar-inertial odometry，BRIO）m估计器，适用于典型无GNSS场景下的四旋翼飞行。在城市与森林中开展的大量真实世界闭环飞行实验表明，该方法对移动物体和虚假目标具备鲁棒性，能保持稳定性能，行驶距离对应的漂移率为0.5%至3.2%。在公开数据集上的基准测试验证了该系统的泛化能力。代码、数据集与视频可在https://github.com/ethz-asl/rio获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
