The Data Market: Policies for Decentralised Visual Localisation

TL;DR

This paper introduces a decentralized data market framework for robots to share navigation data, enabling improved localization through adaptive trading policies and experience map convergence, demonstrated on the Oxford RobotCar Dataset.

Contribution

It proposes a formalized, distributed versioning system for experience maps and a data market approach for resource-efficient, adaptive sharing of localization data among robots.

Findings

Agents improve localization accuracy over time.

Market policies refine and accelerate localization.

System demonstrates effective data sharing on Oxford RobotCar Dataset.

Abstract

This paper presents a mercantile framework for the decentralised sharing of navigation expertise amongst a fleet of robots which perform regular missions into a common but variable environment. We build on our earlier work and allow individual agents to intermittently initiate trades based on a real-time assessment of the nature of their missions or demand for localisation capability, and to choose trading partners with discrimination based on an internally evolving set of beliefs in the expected value of trading with each other member of the team. To this end, we suggest some obligatory properties that a formalisation of the distributed versioning of experience maps should exhibit, to ensure the eventual convergence in the state of each agent's map under a sequence of pairwise exchanges, as well as the uninterrupted integrity of the representation under versioning operations. To mitigate limitations in hardware and network resources, the "data market" is catalogued by distinct sections of the world, which the agents treat as "products" for appraisal and purchase. To this end, we demonstrate and evaluate our system using the publicly available Oxford RobotCar Dataset, the hand-labelled data market catalogue (approaching 446km of fully indexed sections-of-interest) for which we plan to release alongside the existing raw stereo imagery. We show that, by refining market policies over time, agents achieve improved localisation in a directed and accelerated manner.