Automated Construction of Metric Maps using a Stochastic Robotic Swarm Leveraging Received Signal Strength

TL;DR

This paper introduces an automated method for constructing metric maps of unknown environments using a swarm of resource-limited robots that combine noisy signal strength data and odometry, processed with topological data analysis.

Contribution

It presents a novel offline mapping procedure that leverages persistent homology to segment obstacles from uncertain robot data, with theoretical guarantees and simulation validation.

Findings

Effective obstacle segmentation demonstrated in six different simulated domains.

Theoretical proofs of method completeness and analysis of computational complexity.

Successful integration of topological data analysis with robotic mapping.

Abstract

In this work, we present a novel automated procedure for constructing a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots. The robots obtain this data during random exploration of the domain by combining onboard odometry information with noisy measurements of signals received from transmitters located outside the domain. This data is processed offline to compute a density function of the free space over a discretization of the domain. We use persistent homology techniques from topological data analysis to estimate a value for thresholding the density function, thereby segmenting the obstacle-occupied region in the unknown domain. Our approach is substantiated with theoretical results to prove its completeness and to analyze its time complexity. The effectiveness of the procedure is illustrated with numerical simulations conducted on six different domains, each with two signal transmitters.