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On Multi-robot Map Fusion by Inter-robot Observations
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems . Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems . Linköping University, The Institute of Technology.
2009 (English)In: In proceedings of 12th International Conference on Information Fusion, 2009Conference paper, Published paper (Other academic)
Abstract [en]

This paper addresses the problem of aligning and fusing maps built by multiple robots. The proposed method for solving the multi-robot map alignment problem relies on inter-robot observations to seed the alignment processing and find a transformation between the map reference frames. The method enables one to join maps from robots with or without initial correspondence. However, the poses of each robot during an inter-robot observation need to be synchronized. In this work the method is applied onto Collaborative Smoothing and Mapping (C-SAM), a smoothing approach for merging maps that are created by different robots independently or in teams. In contrast to traditional Extended Kalman Filter (EKF) or Particle Filtering (PF) methods the smoothing does not exclude any information and is therefore better equipped to deal with non-linear process and measurement models. The algorithm is also proven to be useful in two different experiments showing the robustness of the algorithm. The experiments show that alignment can be conducted using only inter-robot observation in both unguided terrain as well as in terrain with many false observations. The key contribution of this work is a robust algorithm for solving the association problem and eliminating false observations when doing multi-robot map alignment using inter-robot observations during a rendezvous.

Place, publisher, year, edition, pages
2009.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-15324ISI: 000273560001076OAI: oai:DiVA.org:liu-15324DiVA: diva2:113900
Conference
12th International Conference on Information Fusion
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2010-08-12Bibliographically approved
In thesis
1. Multi-robot Information Fusion: Considering spatial uncertainty models
Open this publication in new window or tab >>Multi-robot Information Fusion: Considering spatial uncertainty models
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The work presented in this thesis covers the topic of deployment for mobile robot teams. By connecting robots in teams they can perform a better job than each individual is capable of. It also gives redundancy, increases robustness, provides scalability, and increases efficiency. Multi-robot Information Fusion also results in a broader perspective for decision making. This thesis focuses on methods for estimating formation and trajectories and how these can be used for deployment of a robot team. The problems covered discuss what impact trajectories and formation have on the total uncertainty when exploring unknown areas. The deployment problem is approached using a centralized Kalman filter, for investigation of how team formation affects error propagation. Trajectory estimation is done using a smoother, where all information is used not only to estimate the trajectory of each robot, but also to align trajectories from different robots. Both simulation and experimental results are presented in the appended papers. It is shown that sensor placements can substantially affect uncertainty during deployment. When deploying a robot team the formation can be used as a tool for balancing error propagation among the robot states. A robust algorithm for associating rendezvous observations to align robot trajectories is also presented. Trajectory alignment is used as an efficient and cost-effective method for joining mapping information within robot teams. When working with robot teams, sensor placement and formation should be considered to obtain the maximum from the system. It is also of great value to mix robots with different characteristics since it is shown that using sensor fusion the robots can inherit each other’s characteristics if sensors are used correctly. Information sharing requires modularity and general models, which consumecomputational resources. Over time computer resources will become cheaper, allowing for distribution, and each robot will become more self-contained. Together with increased wireless bandwidth this will enable larger numbers of robots to cooperate.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. 82 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1209
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15327 (URN)978-91-7393-813-6 (ISBN)
Public defence
2008-09-19, A35, Hus A, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2008-11-05 Created: 2008-10-31 Last updated: 2009-04-22Bibliographically approved

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Andersson, LarsNygårds, Jonas

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