  Location retrieval based on visual information is to retrieve the location of
an agent (e.g. human, robot) or the area they see by comparing the observations
with a certain form of representation of the environment. Existing methods
generally require precise measurement and storage of the observed environment
features, which may not always be robust due to the change of season,
viewpoint, occlusion, etc. They are also challenging to scale up and may not be
applicable for humans due to the lack of measuring/imaging devices. Considering
that humans often use less precise but easily produced qualitative spatial
language and high-level semantic landmarks when describing an environment, a
qualitative location retrieval method is proposed in this work by describing
locations/places using qualitative place signatures (QPS), defined as the
perceived spatial relations between ordered pairs of co-visible landmarks from
viewers' perspective. After dividing the space into place cells each with
individual signatures attached, a coarse-to-fine location retrieval method is
proposed to efficiently identify the possible location(s) of viewers based on
their qualitative observations. The usability and effectiveness of the proposed
method were evaluated using openly available landmark datasets, together with
simulated observations by considering the possible perception error.
