research-article

GRAIL: A Gradients-of-Intensities-based Local Descriptor for Map-based Localization Using LiDAR Sensors

Authors:

Constanze Hungar,

Sara Brakemeier,

Stefan Jürgens,

Frank KösterAuthors Info & Claims

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Pages 4398 - 4403

https://doi.org/10.1109/ITSC.2019.8917525

Published: 01 October 2019 Publication History

Abstract

Localization with respect to a map is an essential objective for the development of highly automated and autonomous vehicles as well as of mobile robots. Commonly, localization solutions rely on static, semantic objects, like road signs or house corners. In this paper, we introduce a novel local descriptor characterizing neighborhoods of LiDAR point clouds without semantic information to be independent of those semantic infrastructure elements. In contrast to other descriptor methods, our system uses the LiDAR sensors’ intensity information which is encoded into a novel gradients-of-intensities-based local descriptor, called GRAIL. The descriptor represents shapes of intensities of the point clouds’ local neighborhoods. We use the proposed descriptor for global localization with respect to a map and compare it to other well-known geometry-based descriptors to complete the localization framework. The introduced method has been thoroughly evaluated on a large data set including both, our own and the KITTI raw data benchmark. The experiments presented in this paper show that the proposed method can achieve accuracy at the level of stateof-the-art descriptor algorithms, even though most of these descriptors use the more informative geometry information.

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Cited By

Wang TWang NXiao JMiao YSun YLi HZhang M(2023)One-shot domain adaptive real-time 3D obstacle detection in farmland based on semantic-geometry-intensity fusion strategyComputers and Electronics in Agriculture10.1016/j.compag.2023.108264214:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108264

Index Terms

GRAIL: A Gradients-of-Intensities-based Local Descriptor for Map-based Localization Using LiDAR Sensors
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

October 2019

4550 pages

Copyright © 2019.

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IEEE Press

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Published: 01 October 2019

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Cited By

Wang TWang NXiao JMiao YSun YLi HZhang M(2023)One-shot domain adaptive real-time 3D obstacle detection in farmland based on semantic-geometry-intensity fusion strategyComputers and Electronics in Agriculture10.1016/j.compag.2023.108264214:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108264

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