Trust-Region Nonlinear Optimization Algorithm for Orientation Estimator and Visual Measurement of Inertial-Magnetic Sensor

This paper proposes a novel robust orientation estimator to enhance the accuracy and robustness of orientation estimation for inertial-magnetic sensors of the small consumer-grade drones. The proposed estimator utilizes a trust-region strategy within a nonlinear optimization framework, transforming the orientation fusion problem into a nonlinear optimization problem based on the maximum likelihood principle. The proposed estimator employs a trust-region Dogleg gradient descent strategy to optimize orientation precision and incorporates a Huber robust kernel to minimize interference caused by acceleration during the maneuvering process of the drone. In addition, a novel method for evaluating the performance of orientation estimators is also presented based on visuals. The proposed method consists of two parts: offline calibration of the basic cube using Augmented Reality University of Cordoba (ArUco) markers and online orientation measurement of the sensor carrier using a nonlinear optimization solver. The proposed measurement method's accuracy and the proposed estimator's performance are evaluated under low-dynamic (rotation) and high-dynamic (shake) conditions in the experiment. The experimental findings indicate that the proposed measurement method obtains an average re-projection error of less than 0.1 pixels. The proposed estimator has the lowest average orientation error compared to conventional orientation estimation algorithms. Despite the time-consuming nature of the proposed estimator, it exhibits greater robustness and precision, particularly in highly dynamic environments.