A Storage-Saving Quadtree-Based Multibeam Bathymetry Map Representation Method

Abstract

With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is acoustic transmission, but its bandwidth is limited. Therefore, how to compress and process multibeam bathymetry maps so that AUVs can acquire maps gathered by other AUVs has become an important topic of study. This study presents a representation approach for multibeam bathymetry maps based on a quadtree structure. In comparison to the girding approach, the sparse pseudo-input Gaussian processes (SPGPs) method, and the octree-based method, the quadtree-based method suggested in this study preserves precision while compressing storage space. Experiments utilizing field data validate the performance of the proposed technique, and the method’s ability to compress storage space towards an AUV cooperative SLAM’s scenario.

A Dual Robust Strategy for Removing Outliers in Multi-Beam Sounding to Improve Seabed Terrain Quality Estimation

“…Seabed terrain data presents significant geomorphic features on the ocean’s surface, offering crucial data support for offshore operations, resource exploitation, benthic organism research, and foundation engineering building [ 1 , 2 ]. As a result, a lot of underwater measurements have been done, particularly since multi-beam echo sounders (MBESs) have become popular and provide an important means for ocean depth measurement.…”