Problem
Research questions and friction points this paper is trying to address.
state estimation
IMU vibration
multirotor UAV
motor angular speed
inertial measurement degradation
Motor Angular Speed Preintegration for Multirotor UAV State Estimation
🤖 AI Summary
This work addresses the significant degradation in state estimation accuracy caused by propeller-induced vibrations distorting IMU measurements in multirotor UAVs. To mitigate this issue, the authors propose a novel motor-speed preintegration method that replaces the conventional IMU for independent state propagation. They further introduce a new factor formulation directly compatible with factor graph optimization frameworks, enabling seamless fusion with LiDAR odometry for high-precision state estimation. Experimental results demonstrate that, compared to LIO-SAM, the proposed approach improves position and velocity estimation accuracy by 28% and 65%, respectively, reduces measurement latency by 14%, and exhibits greater robustness to parameter uncertainties.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
motor angular speed preintegration
IMU-free state estimation
factor graph optimization
LiDAR odometry
UAV state estimation
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
