Smoothing and Mapping using Multiple Robots

TL;DR

This paper extends full SLAM methods to multi-robot systems, enabling smooth, loop-closure maps in static environments through a novel pipeline and prior estimation technique, demonstrated in indoor experiments.

Contribution

It introduces a multi-robot smoothing and mapping system with a mathematical formulation ensuring full bundle adjustment applicability for static environments.

Findings

Effective multi-robot mapping demonstrated in indoor experiments

A prior estimation technique enables large-scale mapping with multiple robots

The system produces smooth, loop-closure maps comparable to single-robot mapping

Abstract

Mapping expansive regions is an arduous and often times incomplete when performed by a single agent. In this paper we illustrate an extension of \texttt{Full SLAM} \cite{Dellaert06ijrr} and \cite{dong}, which ensures smooth maps with loop-closure for multi-robot settings. The current development and the associated mathematical formulation ensure without loss of generality the applicability of full bundle adjustment approach for multiple robots operating in relatively static environments. We illustrate the efficacy of this system by presenting relevant results from experiments performed in an indoor setting. In addition to end-to-end description of the pipeline for performing smoothing and mapping \texttt{SAM} with a fleet of robots, we discuss a one-time prior estimation technique that ensures the incremental concatenation of measurements from respective robots in order to generate one smooth global map - thus emulating large scale mapping with single robot. Along with an interpretation of the non-linear estimates, we present necessary implementation details for adopting this SAM system.