The rise of autonomous systems operating close to humans imposes new challenges in\nterms of robustness and precision on the estimation and control algorithms. Approaches based\non nonlinear optimization, such as moving horizon estimation, have been shown to improve the\naccuracy of the estimated solution compared to traditional filter techniques. This paper introduces\nan optimization-based framework for multi-sensor fusion following a moving horizon scheme.\nThe framework is applied to the often occurring estimation problem of motion tracking by fusing\nmeasurements of a global navigation satellite system receiver and an inertial measurement unit.\nThe resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering\nairplane and is evaluated against an accurate reference trajectory. A detailed study of the influence\nof the horizon length on the quality of the solution is presented and evaluated against filter-like\nand batch solutions of the problem. The versatile configuration possibilities of the framework are\nfinally used to analyze the estimated solutions at different evaluation times exposing a nearly linear\nbehavior of the sensor fusion problem.
Loading....