The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approaches

翻译：导航的目的是确定载人和自主平台、人类和动物的方位、速度和方向。 获得准确的导航通常需要在基于模型的非线性估计框架内将若干传感器,例如惯性传感器和全球导航卫星系统,在一个基于模型的非线性估计框架内进行合并。最近,在各个领域采用的数据驱动方法与基于模型的方法相比,显示了最先进的性能。在本文件中,我们审查了在自主导航和传感器集成实验室(ANSFL)开发和实验验证的多学科、以数据驱动的导航算法,包括适合人类和动物应用的算法、各种自主平台以及多用途导航和聚成法。