Day and Night Collaborative Dynamic Mapping in Unstructured Environment Based on Multimodal Sensors

Yufeng Yue, Chule Yang, Jun Zhang, Mingxing Wen, Zhenyu Wu, Haoyuan Zhang, Danwei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Citations (Scopus)

Abstract

Enabling long-term operation during day and night for collaborative robots requires a comprehensive understanding of the unstructured environment. Besides, in the dynamic environment, robots must be able to recognize dynamic objects and collaboratively build a global map. This paper proposes a novel approach for dynamic collaborative mapping based on multimodal environmental perception. For each mission, robots first apply heterogeneous sensor fusion model to detect humans and separate them to acquire static observations. Then, the collaborative mapping is performed to estimate the relative position between robots and local 3D maps are integrated into a globally consistent 3D map. The experiment is conducted in the day and night rainforest with moving people. The results show the accuracy, robustness, and versatility in 3D map fusion missions.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2981-2987
Number of pages7
ISBN (Electronic)9781728173955
DOIs
Publication statusPublished - May 2020
Externally publishedYes
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: 31 May 202031 Aug 2020

Publication series

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

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/TerritoryFrance
CityParis
Period31/05/2031/08/20

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