Fundamental Limits of Self-localization for Cooperative Robotic Platforms Using Signals of Opportunity

Abstract

A fundamental problem in robotic applications is the localization of the robots. We consider the problem of global self-localization for a robotic platform with autonomous robots using signals of opportunity (SOOP). We first give a brief overview of the state-of-the-art in robotic localization using SOOP, and then propose a scheme that requires minimal prior environmental information, no pre-configuration, and only loose synchronization between the robots. To further analyze the potential for the use of SOOP in robotic localization and to investigate the effect of clock asynchronism, we derive an analytical expression for the equivalent Fisher information matrix of the Cramer-Rao lower bound (CRLB). The derivation is based on the received signal waveform, and allows us to analyze the contributions of various factors to the localization accuracy. The CRLB provides a valuable guideline for the design of a robotic platform in which a desired level of localization accuracy is to be achieved. We also analyze the distortions in the time difference of arrival and frequency difference of arrival measurements caused by different clock offsets and skews at the robots. We propose a robust algorithm to estimate robot location and velocity, which mitigates the clock biases. Simulation results suggest that our proposed algorithm approaches the CRLB when clock skews have small standard deviations.