Girrbach Fabian, Hol Jeroen D, Bellusci Giovanni, Diehl Moritz
Xsens Technologies B.V., Enschede 7521 PR, The Netherlands.
Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany.
Sensors (Basel). 2017 May 19;17(5):1159. doi: 10.3390/s17051159.
The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.
与人类近距离运行的自主系统的兴起，在鲁棒性和精度方面给估计和控制算法带来了新的挑战。与传统滤波技术相比，基于非线性优化的方法，如移动时域估计，已被证明能提高估计解的准确性。本文介绍了一种基于移动时域方案的多传感器融合优化框架。该框架应用于通过融合全球导航卫星系统接收机和惯性测量单元的测量值来进行运动跟踪这一常见的估计问题。所得算法用于估计机动飞机的位置、速度和姿态，并与精确的参考轨迹进行对比评估。文中对时域长度对解的质量的影响进行了详细研究，并与该问题的类似滤波和批处理解进行了对比评估。该框架的多种配置可能性最终被用于分析不同评估时刻的估计解，揭示了传感器融合问题几乎呈线性的行为。
