# Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments

arXiv: https://arxiv.org/abs/2108.08621 · HTML: https://mlsanity.com/paper/2108.08621/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2021.

## Built on by (most cited first, 8 of 8)

- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)
- [SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data](https://mlsanity.com/paper/2209.07951/) (2022)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap](https://mlsanity.com/paper/2307.12116/) (2023)
- [GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration](https://mlsanity.com/paper/2212.12745/) (2022)
- [Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map](https://mlsanity.com/paper/2305.14038/) (2023)
- [L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery](https://mlsanity.com/paper/2503.11245/) (2025)
- [DRIP: Discriminative Rotation-Invariant Pole Landmark Descriptor for 3D LiDAR Localization](https://mlsanity.com/paper/2406.11266/) (2024)

## Abstract (arXiv)

Reliable and accurate localization is crucial for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, lamps, etc., are ideal 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 that runs online and has little computational demands such that this information can be used for a localization system. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point cloud explicitly and enables fast pole extraction for each scan. We test the proposed pole extraction and localization approach on different datasets with different LiDAR scanners, weather conditions, routes, and seasonal changes. The experimental results show that our approach outperforms other state-of-the-art approaches, while running online without a GPU. Besides, we release our pole dataset to the public for evaluating the performance of pole extractor, as well as the implementation of our approach.