A coarse-to-fine LiDar-based SLAM with dynamic object removal in dense urban areas

Feng Huang, Donghui Shen, Weisong Wen, Jiachen Zhang, Li Ta Hsu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Robust and precise localization and mapping are essential for autonomous systems. Light detection and ranging (LiDAR) odometry is extensively studied in the past decades to achieve this goal. However, almost all the LiDAR-based approaches are built on top of the static world assumption. The performance of the LiDAR-based method is significantly degraded in urban canyons with enormous dynamic objects. To tackle this challenge, we propose a coarse-to-fine LiDAR-based solution with dynamic object removal. Both instant-level deep neural network (DNN) and point-wise discrepancy images are adopted to deal with the dynamic points. The evaluation results show that a 19.1% improvement of the LiDAR-based method in a highly urbanized area can be achieved by distinguishing dynamic objects from LiDAR scan while generating clean maps for real-world representation.

Original languageEnglish
Title of host publicationProceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021
PublisherInstitute of Navigation
Pages3162-3172
Number of pages11
ISBN (Electronic)9780936406299
DOIs
Publication statusPublished - Sep 2021
Event34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 - St. Louis, United States
Duration: 20 Sep 202124 Sep 2021

Publication series

NameProceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021

Conference

Conference34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021
Country/TerritoryUnited States
CitySt. Louis
Period20/09/2124/09/21

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

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