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C-SAM: Multi-Robot SLAM using Square Root Information Smoothing
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.
2008 (English)In: Proceedings of International Conference on Robotics and Automation, ICRA 2008, Pasadena, CA, USA, 19th–23rd May, IEEE Xplore , 2008, 2798-2805 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents collaborative smoothing and mapping (C-SAM) as a viable approach to the multi-robot map- alignment problem. This method enables a team of robots to build joint maps with or without initial knowledge of their relative poses. To accomplish the simultaneous localization and mapping this method uses square root information smoothing (SRIS). In contrast to traditional extended Kalman filter (EKF) methods the smoothing does not exclude any information and is therefore also better equipped to deal with non-linear process and measurement models. The method proposed does not require the collaborative robots to have initial correspondence. The key contribution of this work is an optimal smoothing algorithm for merging maps that are created by different robots independently or in groups. The method not only joins maps from different robots, it also recovers the complete robot trajectory for each robot involved in the map joining. It is also shown how data association between duplicate features is done and how this reduces uncertainty in the complete map. Two simulated scenarios are presented where the C-SAM algorithm is applied on two individually created maps. One basically joins two maps resulting in a large map while the other shows a scenario where sensor extension is carried out.

Place, publisher, year, edition, pages
IEEE Xplore , 2008. 2798-2805 p.
Keyword [en]
C-SAM, SLAM, fusion, multi, robot
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-15322DOI: 10.1109/ROBOT.2008.4543634ISBN: 978-1-4244-1646-2 (print)OAI: oai:DiVA.org:liu-15322DiVA: diva2:113898
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2009-04-22Bibliographically 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, Lars A. A.Nygårds, Jonas

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