用电机转速预积分提升无人机状态估计精度，不依赖惯性传感器。
Motor Angular Speed Preintegration for Multirotor UAV State Estimation
- 通过电机转速计算加速度并预积分，替代传统IMU进行状态传播。
- 相比主流算法，位置误差降28%，速度误差降65%，延迟低14%。
- 适合对振动敏感的高速飞行场景，开源代码可直接使用。
精确的状态估计对实现无人机敏捷、近障碍物飞行的闭环控制至关重要。当前方法通常融合低频位姿测量与高频惯性测量以获得高精度状态估计，但机载IMU受旋翼振动影响，导致精度下降。本文提出一种基于电机转速预积分加速度的新方法，证明该加速度可独立用于状态传播，无需引入IMU。进一步设计了一种可直接嵌入因子图优化框架的电机转速预积分因子。将该因子与激光雷达数据结合，提出电机转速激光雷达里程计（MAS-LO）算法，并已开源。实验对比先进惯性算法LIO-SAM，显示位置估计精度提升28%，速度估计精度提升65%，测量延迟降低14%，且对参数误设具有强鲁棒性。
原文摘要 · Abstract (English)
A precise state estimate is crucial for a tight feedback control that enables agile and near-obstacle flights of UAVs. The state-of-the-art methods fuse slow pose measurements with high-frequency inertial measurements to obtain a precise state estimate. However, the inertial measurements from the IMU onboard the UAV are degraded by vibrations from spinning propellers and the precision of the estimated state suffers. We propose a novel approach based on the preintegration of accelerations obtained from motor speeds. We show that the accelerations obtained in this manner can be used for state propagation on their own to achieve better precision without including the IMU. Further, we propose a factor composed of the preintegrated motor speeds that can be directly employed in factor graph optimization frameworks. We combine our factor with LiDAR measurements into the proposed Motor Angular Speed LiDAR Odometry (MAS-LO) algorithm for precise state estimation, which we open-source. Lastly, we evaluate the estimation precision against a state-of-the-art inertial algorithm LIO-SAM to show 28% improvement in position and 65% in velocity estimation accuracy, 14% lower measurement lag, and high robustness to wrong parameter values.
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