Improvement of 3D-SLAM Accuracy by Removing Moving Objects on 3D-LiDAR Point Cloud Using Image Recognition in Web Camera

Jun Konno, Yoshinobu Ando

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

Abstract

In recent years, robots have been developed that can move autonomously in human environments such as restaurants and airports. For such autonomous mobility, it is important to create a map in advance, and a typical example is Simultaneous Localization and Mapping (SLAM). We created a 3D perception filter that is capable of detecting and eliminating moving point clusters from the input point cloud taken in an indoor environment. In this study, we propose a system that detects moving objects based on camera image recognition and uses the results to construct a more accurate map by minimizing the influence of pedestrians.

Original languageEnglish
Title of host publication2022 22nd International Conference on Control, Automation and Systems, ICCAS 2022
PublisherIEEE Computer Society
Pages527-531
Number of pages5
ISBN (Electronic)9788993215243
DOIs
Publication statusPublished - 2022
Event22nd International Conference on Control, Automation and Systems, ICCAS 2022 - Busan, Korea, Republic of
Duration: 2022 Nov 272022 Dec 1

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2022-November
ISSN (Print)1598-7833

Conference

Conference22nd International Conference on Control, Automation and Systems, ICCAS 2022
Country/TerritoryKorea, Republic of
CityBusan
Period22/11/2722/12/1

Keywords

  • 3D SLAM
  • Dynamic objects
  • Object recognition.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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