Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
FRAME: Fast and Robust Autonomous 3D Point Cloud Map-Merging for Egocentric Multi-Robot Exploration
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-0108-6286
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8235-2728
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2023 (English)In: 2023 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 3483-3489Conference paper, Published paper (Refereed)
Abstract [en]

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 3483-3489
National Category
Robotics and automation
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-101166DOI: 10.1109/ICRA48891.2023.10160771ISI: 001036713002123Scopus ID: 2-s2.0-85168139108ISBN: 979-8-3503-2366-5 (print)ISBN: 979-8-3503-2365-8 (electronic)OAI: oai:DiVA.org:ltu-101166DiVA, id: diva2:1793842
Conference
IEEE International Conference on Robotics and Automation (ICRA 2023), 29 May - 02 June, 2023, London, United Kingdom
Funder
EU, Horizon 2020, 101003591 NEX-GEN SIMSAvailable from: 2023-09-04 Created: 2023-09-04 Last updated: 2025-02-09Bibliographically approved
In thesis
1. On Autonomous Map-merging for Multi-Robot Systems
Open this publication in new window or tab >>On Autonomous Map-merging for Multi-Robot Systems
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Three-dimensional (3D) point cloud map merging is a pivotal technology in robotics and automation, enabling the integration of multiple 3D point cloud maps into a single, comprehensive representation of the environment. This technique is particularly advantageous in multi-robot coordination, where multiple robots collaborate to explore and map extensive areas. Each robot generates a local map within its local frame, which serves as crucial data for localization, collision avoidance, navigation, and path planning, and can later be shared and fused into a global map. In addition, human operators in the industry can find this advantageous, as it allows for faster and more efficient inspections without the need for manual map alignment. This thesis introduces a modular framework for autonomous 3D point cloud map merging in multi-robot systems, addressing the challenge of aligning local maps by identifying acceptable spatial coordinate transformations. This framework facilitates real-time map merging during multi-robot exploration, enhancing mapping efficiency by preventing redundant exploration of already mapped areas. The first contribution stems from formulating and addressing the map merging problem through a modular pipeline and evaluating each component. Then two methods are presented that improve place recognition performance, a fundamental aspect of the process. The first method extends the place recognition pipeline with a topological classification module, enhancing performance in challenging environments and autonomously triggering the map merging pipeline for higher success rates. The second method integrates additional data modalities, such as an inexpensive Wi-Fi module, to enhance place recognition performance. Furthermore, the thesis addresses communication challenges in multi-robot systems. A solution for centralized systems is proposed, where a control mechanism regulates map data transmission to ensure critical information is preserved and the map merging process is not compromised. Additionally, a combined solution for place recognition descriptors is presented, which compresses LiDAR data to improve transmission efficiency. Finally, the map merging framework serves as the backbone of a change detection algorithm. Both the map merging framework and the change detection algorithm are evaluated through a series of use-case deployments, including autonomous Unmanned Aerial Vehicles (UAVs) operations in mining areas and a safety inspection mission following a real blast. 

Place, publisher, year, edition, pages
Luleå University of Technology, 2024. p. 234
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Robotics, Multi-Robot Systems, Map merging
National Category
Robotics and automation
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-110608 (URN)978-91-8048-696-5 (ISBN)978-91-8048-697-2 (ISBN)
Presentation
2024-12-06, A3024, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2024-10-31 Created: 2024-10-31 Last updated: 2025-02-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Stathoulopoulos, NikolaosKoval, AntonNikolakopoulos, George

Search in DiVA

By author/editor
Stathoulopoulos, NikolaosKoval, AntonNikolakopoulos, George
By organisation
Signals and Systems
Robotics and automation

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 110 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf