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Robust and accurate localization is a basic requirement for mobile autonomoussystems. Pole-like objects, such as traffic signs, poles, and lamps arefrequently used landmarks for localization in urban environments due to theirlocal distinctiveness and long-term stability. In this paper, we present anovel, accurate, and fast pole extraction approach based on geometric featuresthat runs online and has little computational demands. Our method performs allcomputations directly on range images generated from 3D LiDAR scans, whichavoids processing 3D point clouds explicitly and enables fast pole extractionfor each scan. We further use the extracted poles as pseudo labels to train adeep neural network for online range image-based pole segmentation. We testboth our geometric and learning-based pole extraction methods for localizationon different datasets with different LiDAR scanners, routes, and seasonalchanges. The experimental results show that our methods outperform otherstate-of-the-art approaches. Moreover, boosted with pseudo pole labelsextracted from multiple datasets, our learning-based method can run acrossdifferent datasets and achieve even better localization results compared to ourgeometry-based method. We released our pole datasets to the public forevaluating the performance of pole extractors, as well as the implementation ofour approach.

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