Distributed Dual Quaternion Based Localization of Visual Sensor Networks

TL;DR

This paper introduces a novel distributed localization method for visual sensor networks using dual quaternion algebra, enhancing accuracy and robustness by unifying position and orientation estimation.

Contribution

It proposes a dual quaternion-based estimation scheme that simplifies and improves localization accuracy without separate pose estimators.

Findings

Improved localization accuracy over traditional methods

Robustness to initial conditions demonstrated

Unified pose estimation enhances performance

Abstract

In this paper we consider the localization problem for a visual sensor network. Inspired by the alternate attitude and position distributed optimization framework discussed in [1], we propose an estimation scheme that exploits the unit dual quaternion algebra to describe the sensors pose. This representation is beneficial in the formulation of the optimization scheme allowing to solve the localization problem without designing two interlaced position and orientation estimators, thus improving the estimation error distribution over the two pose components and the overall localization performance. Furthermore, the numerical experimentation asserts the robustness of the proposed algorithm w.r.t. the initial conditions.