Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

TL;DR

This paper reviews recent data-driven navigation algorithms that fuse multiple sensors for accurate positioning and orientation across various platforms, demonstrating experimental validation and multidisciplinary applications.

Contribution

It provides a comprehensive review of novel data-driven navigation methods developed and validated at ANSFL, highlighting their versatility and experimental success.

Findings

Data-driven approaches outperform traditional model-based methods in navigation accuracy.

Algorithms are applicable to humans, animals, and autonomous platforms.

Experimental validation confirms effectiveness across diverse scenarios.

Abstract

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