Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps

Muhammad Ibrahim, Naveed Akhtar, Saeed Anwar, Michael Wise, Ajmal Mian

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a 360° LiDAR scan to leverage its axial properties. Our model, called Slice Transformer, employs multi-head attention while systematically processing the slices. To the best of our knowledge, this is the first instance of leveraging multi-head attention for outdoor point clouds. We additionally introduce the Perth-Wadataset, which provides a large-scale LiDAR map of Perth city in Western Australia, covering 4km2area. Localization annotations are provided for Perth - Wa.The proposed localization method is thoroughly evaluated on Perth-WA and Appollo-SouthBay datasets. We also establish the efficacy of our self-supervised learning approach for the common downstream task of object classification using ModelNet40 and ScanNN datasets. The code and Perth-WA data will be publicly released.

Original languageEnglish
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages11763-11770
Number of pages8
ISBN (Electronic)9798350323658
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN (Print)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23

Fingerprint

Dive into the research topics of 'Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps'. Together they form a unique fingerprint.
  • 3D Scene understanding from LiDAR point clouds

    Ibrahim, M., 2023, (Unpublished)

    Research output: ThesisDoctoral Thesis

    File
    59 Downloads (Pure)

Cite this