Concurrent Flow-Based Localization and Mapping in Time-Invariant Flow Fields

TL;DR

This paper introduces FLAM, a novel flow-based localization and mapping method for autonomous robots navigating in flow fields, improving localization accuracy and flow field estimation in GPS-denied environments.

Contribution

The paper presents a full SLAM formulation for flow-based localization and mapping, specifically designed for continuous flow fields, extending traditional SLAM to new application domains.

Findings

FLAM improves localization accuracy in simulated flow fields.

FLAM provides consistent flow field mapping with in-situ observations.

FLAM results in smooth robot trajectory estimates.

Abstract

We present the concept of concurrent flow-based localization and mapping (FLAM) for autonomous field robots navigating within background flows. Different from the classical simultaneous localization and mapping (SLAM) problem, where the robot interacts with discrete features, FLAM utilizes the continuous flow fields as navigation references for mobile robots and provides flow field mapping capability with in-situ flow velocity observations. This approach is of importance to underwater vehicles in mid-depth oceans or aerial vehicles in GPS-denied atmospheric circulations. This article introduces the formulation of FLAM as a full SLAM solution motivated by the feature-based GraphSLAM framework. The performance of FLAM was demonstrated through simulation within artificial flow fields that represent typical geophysical circulation phenomena: a steady single-gyre flow field and a double-gyre flow field with unsteady turbulent perturbations. The results indicate that FLAM provides significant improvements in the robots' localization accuracy and a consistent approximation of the background flow field. It is also shown that FLAM leads to smooth robot trajectory estimates.