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.

翻译：在基于三维点云的视觉自定位中，杆状地标因其在季节和天气变化下的长期稳定性，作为实现精确可靠定位的地标具有巨大潜力。本研究旨在探索利用近期发展的深度学习模型进行杆状地标分类，以应用于基于杆状地标的自定位场景。具体而言，所提出的方案包含两个主要模块：杆状地图匹配与杆状类别匹配。在前一模块中，构建局部杆状地图并将其配置与预计算的全局杆状地图进行比对。通过采用高效的RANSAC地图匹配算法，在计算效率与精度之间取得良好平衡。在后一杆状类别匹配模块中，通过杆状属性类别进一步比较由RANSAC地图匹配配对的局部与全局杆状地标。为此，采用k-means聚类以自监督方式学习预定义的伪杆状类别集合。基于公开NCLT数据集的实验表明，与基线方法相比，杆状地标分类方法对视觉自定位系统具有改进效果。