Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM

TL;DR

This paper introduces a neural network approach for loop closure detection in bathymetric point clouds, enhancing underwater SLAM by addressing the challenges of seabed data scarcity and low resolution.

Contribution

It presents a novel neural network architecture trained on real bathymetric data, demonstrating its effectiveness in loop closure detection and point cloud alignment for underwater SLAM.

Findings

Effective loop closure detection in bathymetric data

Improved coarse point cloud alignment

Potential outperforming traditional methods

Abstract

Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is particularly challenging due to the scarcity of identifiable landmarks in the seabed, the large drift in dead-reckoning navigation estimates to which AUVs are prone and the low resolution characteristic of MBES data. Deep learning solutions to loop closure detection have shown excellent performance on data from more structured environments. However, their transfer to the seabed domain is not immediate and efforts to port them are hindered by the lack of bathymetric datasets. Thus, in this paper we propose a neural network architecture aimed to showcase the potential of adapting such techniques to correspondence matching in bathymetric data. We train our framework on real bathymetry from an AUV mission and evaluate its performance on the tasks of loop closure detection and coarse point cloud alignment. Finally, we show its potential against a more traditional method and release both its implementation and the dataset used.