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

arXiv: https://arxiv.org/abs/2208.07364 · HTML: https://mlsanity.com/paper/2208.07364/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [OverlapNet: Loop Closing for LiDAR-based SLAM](https://mlsanity.com/paper/2105.11344/) (2021)
- [Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data](https://mlsanity.com/paper/2105.08971/) (2021)
- [Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform](https://mlsanity.com/paper/2008.09162/) (2020)
- [Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation](https://mlsanity.com/paper/2201.04501/) (2022)
- [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021)
- [Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization](https://mlsanity.com/paper/1908.03274/) (2019)
- [Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping](https://mlsanity.com/paper/1810.06802/) (2018)
- [Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs](https://mlsanity.com/paper/1802.08960/) (2018)
- [Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans](https://mlsanity.com/paper/1910.10550/) (2019)
- [Learning an Overlap-based Observation Model for 3D LiDAR Localization](https://mlsanity.com/paper/2105.11717/) (2021)
- [Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2108.08621/) (2021)
- [Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU](https://mlsanity.com/paper/1902.10194/) (2019)
- [Robust Onboard Localization in Changing Environments Exploiting Text Spotting](https://mlsanity.com/paper/2203.12647/) (2022)
- [Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments](https://mlsanity.com/paper/2003.01875/) (2020)

## Built on by (most cited first, 12 of 12)

- [SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization](https://mlsanity.com/paper/2310.19795/) (2023)
- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)
- [MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities](https://mlsanity.com/paper/2405.17419/) (2024)
- [Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models](https://mlsanity.com/paper/2501.18592/) (2025)
- [Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision](https://mlsanity.com/paper/2407.01518/) (2024)
- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [Charts Are Not Images: On the Challenges of Scientific Chart Editing](https://mlsanity.com/paper/2512.00752/) (2025)
- [MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions](https://mlsanity.com/paper/2403.07593/) (2024)
- [Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map](https://mlsanity.com/paper/2305.14038/) (2023)
- [Map-aided annotation for pole base detection](https://mlsanity.com/paper/2403.01868/) (2024)
- [DRIP: Discriminative Rotation-Invariant Pole Landmark Descriptor for 3D LiDAR Localization](https://mlsanity.com/paper/2406.11266/) (2024)
- [Automatic Image Annotation for Mapped Features Detection](https://mlsanity.com/paper/2412.10438/) (2024)

## Abstract (arXiv)

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.