Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation
This work addresses navigation challenges for autonomous platforms, humans, and animals, but it is incremental as it reviews existing algorithms rather than introducing new ones.
The paper reviews data-driven navigation algorithms developed at the Autonomous Navigation and Sensor Fusion Lab, tackling the problem of accurate navigation for various platforms by applying data-driven methods, with results showing state-of-the-art performance compared to model-based approaches.
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