Concurrent Flow-Based Localization and Mapping in Time-Invariant Flow Fields
This addresses the challenge of localization and mapping for underwater or aerial vehicles in GPS-denied environments with background flows, representing an incremental advancement over classical SLAM by utilizing continuous flow fields instead of discrete features.
The paper tackles the problem of autonomous robot navigation in continuous flow fields by introducing concurrent flow-based localization and mapping (FLAM), which improves localization accuracy and provides consistent flow field approximations, as demonstrated in simulations with steady and unsteady flow fields.
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.