RadarSLAM: Radar based large-scale SLAM in all weathers

Ziyang Hong, Yvan Petillot, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall.

Original languageEnglish
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages5164-5170
Number of pages7
ISBN (Electronic)9781728162126
DOIs
Publication statusPublished - 10 Feb 2021
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, United States
Duration: 25 Oct 202029 Oct 2020
https://www.iros2020.org/

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2020
Country/TerritoryUnited States
CityLas Vegas
Period25/10/2029/10/20
Internet address

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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