  In 3D point cloud-based visual self-localization, pole landmarks have a great
potential as landmarks for accurate and reliable localization due to their
long-term stability under seasonal and weather changes. In this study, we aim
to explore the use of recently developed deep learning models for pole
classification in the context of pole landmark-based self-localization.
Specifically, the proposed scheme consists of two main modules: pole map
matching and pole class matching. In the former module, local pole map is
constructed and its configuration is compared against a precomputed global pole
map. An efficient RANSAC map matching is employed to achieve a good tradeoff
between computational efficiency and accuracy. In the latter pole class
matching module, the local and global poles paired by the RANSAC map-matching
are further compared by means of pole attribute class. To this end, a
predefined set of pseudo pole classes is learned via k-means clustering in a
self-supervised manner. Experiments using publicly available NCLT dataset
showed that the pole-like landmark classification method has an improved effect
on the visual self-localization system compared with the baseline method.
