Shape BoW: Generalized Bag of Words for Appearance-Based Loop Closure Detection in Bathymetric SLAM

Existing bathymetric simultaneous localization and mapping (SLAM) methods predominantly rely on odometry information for loop closure detection, which has a deteriorating performance when handling unreliable odometry data or conducting large-scale mapping missions. This letter introduces a novel generalized Bag of Words (BoW) named Shape BoW (S-BoW) for appearance-based loop closure detection in bathymetric SLAM. S-BoW is trained from the collection of the terrain gradient features extracted from existing bathymetric datasets and can be used in various bathymetric scenarios. We integrated the loop closure detection method using S-BoW into a feature-based bathymetric SLAM method called TTT SLAM, and we evaluated its performance against three existing bathymetric SLAM methods using two datasets. The results indicate that S-BoW not only serves as a generalized BoW but also enhances the efficiency of the integrated SLAM method, achieving accuracy comparable to the original TTT SLAM while offering a 37$\%$ speed improvement in a large-scale sea trial dataset. To the best of our knowledge, S-BoW is the first generalized BoW that can be used to realize effective appearance-based loop closure detection in bathymetric SLAM.