  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.
