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.

本研究旨在探讨基于杆类地标的深度学习模型在视觉定位中的应用，其中考虑了极致稳定性下的杆类地标作为定位的一种可靠标志，并提出了两个关键模块：杆类地图匹配和杆类属性匹配，采取 RANSAC 地图匹配方法以在计算效率和精度之间达成良好的平衡。实验证明，与基线方法相比，杆类地标分类方法对视觉定位系统的提升更加明显。