Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Features

TL;DR

This paper introduces a resource-efficient, distributed 3D map registration method that leverages tomographic features from 2D projections, significantly reducing memory and computation while enabling parallel processing.

Contribution

It presents a novel 3D map merging algorithm using tomographic features that improves efficiency and reduces resource consumption compared to existing methods.

Findings

Reduces memory use and execution time by an order of magnitude.

Enables efficient parallelizable 3D map registration.

Demonstrates effectiveness through experimental studies.

Abstract

A robust, resource-efficient, distributed, and minimally parameterized 3D map matching and merging algorithm is proposed. The suggested algorithm utilizes tomographic features from 2D projections of horizontal cross-sections of gravity-aligned local maps, and matches these projection slices at all possible height differences, enabling the estimation of four degrees of freedom in an efficient and parallelizable manner. The advocated algorithm improves state-of-the-art feature extraction and registration pipelines by an order of magnitude in memory use and execution time. Experimental studies are offered to investigate the efficiency of this 3D map merging scheme.