Visual-Inertial-Aided Online MAV System Identification
Chuchu Chen, Yulin Yang, Patrick Geneva, Woosik Lee, Guoquan Huang
Abstract
System modeling and parameter identification of micro aerial vehicles (MAV) are crucial for robust autonomy, especially under highly dynamic motions. Visual-inertial-aided online parameter identification has recently seen research attention due to the demanding of adaptation to platform configuration changes with minimal onboard sensor requirements. To this end, we design an online MAV system identification algorithm to tightly fuse visual, inertial and MAV aerodynamic information within a lightweight multi-state constraint Kalman filter (MSCKF) framework. In particular, while one could blindly fuse the MAV dynamic-induced relative motion constraints in EKF, we numerically show that due to the (quadrotor) MAV system modeling inaccuracy, they often become overconfident and negatively impact the state estimates. As such, we leverage the Schmidt-Kalman filter (SKF) for MAV system parameter identification to prevent corruption of state estimates. Through extensive simulations and real-world experiments, we validate the proposed SKF-based scheme and demonstrate its ability to perform robust system identification even in the presence of an inconsistent MAV dynamic model under different motions.