A Distributed Gaussian Process Model for Multi-Robot Mapping

TL;DR

This paper introduces DistGP, a distributed Gaussian process framework for multi-robot mapping that enables collaborative, online, and robust learning of a global function using local data and belief propagation.

Contribution

It presents a novel distributed Gaussian process model that can be trained online with dynamic connectivity, outperforming existing tree-structured GPs and neural network optimizers.

Findings

DistGP achieves comparable performance to centralized models.

It is more robust to sparse communication.

DistGP better supports continual learning.

Abstract

We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (GP) model with a factorisation that mirrors the multi-robot structure of the task, and admits distributed training via Gaussian belief propagation (GBP). Our loopy model outperforms Tree-Structured GPs \cite{bui2014tree} and can be trained online and in settings with dynamic connectivity. We show that such distributed, asynchronous training can reach the same performance as a centralised, batch-trained model, albeit with slower convergence. Last, we compare to DiNNO \cite{yu2022dinno}, a distributed neural network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.