  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.
