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