# Simultaneous merging multiple grid maps using the robust motion averaging

## TL;DR

This paper introduces a robust motion averaging method for merging multiple grid maps simultaneously, improving the integration process in multi-robot mapping within GPS-denied environments.

## Contribution

It proposes a novel approach that uses reliable relative motions and motion averaging to merge multiple grid maps efficiently and accurately.

## Key findings

- Effective merging of multiple maps demonstrated on real robot data.
- Robust removal of unreliable relative motions improves accuracy.
- Approach outperforms existing methods in large environment mapping.

## Abstract

Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that proposed approach can achieve simultaneous merging of multiple grid maps with good performances.