Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

TL;DR

This paper introduces a fast, online pole extraction method from range images for urban LiDAR-based localization, combining geometric features and deep learning, outperforming state-of-the-art approaches across various datasets.

Contribution

The paper presents a novel geometric and learning-based pole extraction approach that operates online on range images, improving localization accuracy and efficiency in urban environments.

Findings

Our methods outperform existing state-of-the-art pole extraction techniques.

The learning-based approach generalizes well across different datasets and conditions.

Pseudo labels from multiple datasets enhance localization performance.

Abstract

Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learning-based pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other state-of-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach.