In 3D LiDAR-based robot self-localization, pole-like landmarks are gaining
popularity as lightweight and discriminative landmarks. This work introduces a
novel approach called "discriminative rotation-invariant poles," which enhances
the discriminability of pole-like landmarks while maintaining their lightweight
nature. Unlike conventional methods that model a pole landmark as a 3D line
segment perpendicular to the ground, we propose a simple yet powerful approach
that includes not only the line segment's main body but also its surrounding
local region of interest (ROI) as part of the pole landmark. Specifically, we
describe the appearance, geometry, and semantic features within this ROI to
improve the discriminability of the pole landmark. Since such pole landmarks
are no longer rotation-invariant, we introduce a novel rotation-invariant
convolutional neural network that automatically and efficiently extracts
rotation-invariant features from input point clouds for recognition.
Furthermore, we train a pole dictionary through unsupervised learning and use
it to compress poles into compact pole words, thereby significantly reducing
real-time costs while maintaining optimal self-localization performance. Monte
Carlo localization experiments using publicly available NCLT dataset
demonstrate that the proposed method improves a state-of-the-art pole-based
localization framework.

本文提出了一种名为 “具有鉴别性的旋转不变极点” 的新方法，它增强了轻量级且具有鉴别性的极点的特征，在保持其轻量级的同时提高了其鉴别性。通过在极点中包括其主体线段以及周围感兴趣区域（ROI）来改进极点的鉴别性，同时描述了该 ROI 内的外观、几何和语义特征以提高极点的鉴别性。此外，引入了一种新型的旋转不变卷积神经网络来从输入点云中自动有效地提取旋转不变特征，还通过无监督学习训练一个极点字典，并将极点压缩成紧凑的极点词，从而在保持最佳自我定位性能的同时显著降低了实时成本。使用公开可用的 NCLT 数据集进行的蒙特卡洛定位实验表明，所提出的方法改进了现有极点定位框架的性能。