  We present an online landmark selection method for distributed long-term
visual localization systems in bandwidth-constrained environments. Sharing a
common map for online localization provides a fleet of au- tonomous vehicles
with the possibility to maintain and access a consistent map source, and
therefore reduce redundancy while increasing efficiency. However, connectivity
over a mobile network imposes strict bandwidth constraints and thus the need to
minimize the amount of exchanged data. The wide range of varying appearance
conditions encountered during long-term visual localization offers the
potential to reduce data usage by extracting only those visual cues which are
relevant at the given time. Motivated by this, we propose an unsupervised
method of adaptively selecting landmarks according to how likely these
landmarks are to be observable under the prevailing appear- ance condition. The
ranking function this selection is based upon exploits landmark
co-observability statistics collected in past traversals through the mapped
area. Evaluation is per- formed over different outdoor environments, large
time-scales and varying appearance conditions, including the extreme tran-
sition from day-time to night-time, demonstrating that with our
appearance-dependent selection method, we can significantly reduce the amount
of landmarks used for localization while maintaining or even improving the
localization performance.
