  For autonomous vehicles to operate persistently in a typical urban
environment, it is essential to have high accuracy position information. This
requires a mapping and localisation system that can adapt to changes over time.
A localisation approach based on a single-survey map will not be suitable for
long-term operation as it does not incorporate variations in the environment.
In this paper, we present new algorithms to maintain a featured-based map. A
map maintenance pipeline is proposed that can continuously update a map with
the most relevant features taking advantage of the changes in the surroundings.
Our pipeline detects and removes transient features based on their geometrical
relationships with the vehicle's pose. Newly identified features became part of
a new feature map and are assessed by the pipeline as candidates for the
localisation map. By purging out-of-date features and adding newly detected
features, we continually update the prior map to more accurately represent the
most recent environment. We have validated our approach using the USyd Campus
Dataset, which includes more than 18 months of data. The results presented
demonstrate that our maintenance pipeline produces a resilient map which can
provide sustained localisation performance over time.
