Localizability-Constrained Deployment of Mobile Robotic Networks with Noisy Range Measurements

TL;DR

This paper introduces a method for deploying mobile robotic networks that optimizes their geometry for better localization accuracy using noisy range measurements, through a localizability function derived from the Cramér-Rao bound.

Contribution

It proposes a gradient descent-based motion planning approach that optimizes network geometry for localization, linking statistical bounds with graph rigidity in a distributed framework.

Findings

Gradient descent motion planners improve localization accuracy.

The localizability function effectively guides robot deployment.

Connections between statistical bounds and graph rigidity are established.

Abstract

When nodes in a mobile network use relative noisy measurements with respect to their neighbors to estimate their positions, the overall connectivity and geometry of the measurement network has a critical influence on the achievable localization accuracy. This paper considers the problem of deploying a mobile robotic network implementing a cooperative localization scheme based on range measurements only, while attempting to maintain a network geometry that is favorable to estimating the robots' positions with high accuracy. The quality of the network geometry is measured by a "localizability" function serving as potential field for robot motion planning. This function is built from the Cram\'er-Rao bound, which provides for a given geometry a lower bound on the covariance matrix achievable by any unbiased position estimator that the robots might implement using their relative measurements. We describe gradient descent-based motion planners for the robots that attempt to optimize or constrain different variations of the network's localizability function, and discuss ways of implementing these controllers in a distributed manner. Finally, the paper also establishes formal connections between our statistical point of view and maintaining a form of weighted rigidity for the graph capturing the relative range measurements.