Pole-Image: A Self-Supervised Pole-Anchored Descriptor for Long-Term LiDAR Localization and Map Maintenance

TL;DR

This paper introduces Pole-Image, a novel pole-anchored descriptor for LiDAR-based long-term robot localization and map maintenance, leveraging a hybrid polar image representation and contrastive learning.

Contribution

It proposes a new pole-based representation called Pole-Image and a contrastive learning approach to generate robust, viewpoint-invariant descriptors for improved localization and map maintenance.

Findings

Achieves robust self-localization despite perceptual aliasing.

Enables high-sensitivity change detection for map maintenance.

Facilitates large-scale data collection through easy pole detection.

Abstract

Long-term autonomy for mobile robots requires both robust self-localization and reliable map maintenance. Conventional landmark-based methods face a fundamental trade-off between landmarks with high detectability but low distinctiveness (e.g., poles) and those with high distinctiveness but difficult stable detection (e.g., local point cloud structures). This work addresses the challenge of descriptively identifying a unique "signature" (local point cloud) by leveraging a detectable, high-precision "anchor" (like a pole). To solve this, we propose a novel canonical representation, "Pole-Image," as a hybrid method that uses poles as anchors to generate signatures from the surrounding 3D structure. Pole-Image represents a pole-like landmark and its surrounding environment, detected from a LiDAR point cloud, as a 2D polar coordinate image with the pole itself as the origin. This representation leverages the pole's nature as a high-precision reference point, explicitly encoding the "relative geometry" between the stable pole and the variable surrounding point cloud. The key advantage of pole landmarks is that "detection" is extremely easy. This ease of detection allows the robot to easily track the same pole, enabling the automatic and large-scale collection of diverse observational data (positive pairs). This data acquisition feasibility makes "Contrastive Learning (CL)" applicable. By applying CL, the model learns a viewpoint-invariant and highly discriminative descriptor. The contributions are twofold: 1) The descriptor overcomes perceptual aliasing, enabling robust self-localization. 2) The high-precision encoding enables high-sensitivity change detection, contributing to map maintenance.