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Online Interactive Building of Presence

Online Interactive Building of Presence. Suomela J ., Saarinen J., Halme A., Harmo P. Description of PeLoTe-project. Project funded by the European Community under the IST programme Future and Emerging Technologies. Partners.

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Online Interactive Building of Presence

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  1. Online Interactive Building of Presence Suomela J., Saarinen J., Halme A., Harmo P. Description of PeLoTe-project Project funded by the European Community under the IST programme Future and Emerging Technologies Jussi Suomela

  2. Partners • CTU: Czech Technical University, Gerstner Laboratory (CZ) Coordinator • Certicon: CertiCon a.s. (CZ) • HUT: Helsinki University of Technology, Automation Technology Lab.(FI) • JMUW: Bayerische Julius-Maximilians Universität Würzburg (DE) • ARS: Steinbeis GmbH & Co. für Technologietransfer (DE) Jussi Suomela

  3. IntroductionPeLoTe = Building Presence through Localization for Hybrid Telematic Systems Scenario • Human and Robotic entities (HE, RE) explore common area • Both provide continuous mapping data from environment • Mapping information is processed to a common presence for both entities • Applications: Rescue, military, planetary, etc. Jussi Suomela

  4. Methods to be developed • Personal Navigation (Human dead reckoning) • human localization indoors • Human SLAM • human localization and mapping • Cooperative localization • Beacon based localization with the help of robots • Robot SLAM • Localization and mapping • Forming the presence • Common presence from different type of data Jussi Suomela

  5. Human Entities (HE) • Verbal mapping information • Automatic localization with human dead reckoning and robot beacons • Human SLAM based onboard laser scanner, if feasible Jussi Suomela

  6. Personal Navigation (PeNa) • In rescue situation ”the location of all men has to be known” • To map the environment the position has to be know •  human positioning needed!! (as accurate as possible) • In indoor conditions  commercial beacons (GPS, Cellular, etc.) can’t be used •  Human dead-reckoning needed •  Own beacon system to correct the accumulated error • Dead reckoning + robot aided localization Jussi Suomela

  7. PeNa: Dead reckoning • DRM (dead reckoning module, commercial) • pedometer (inertial), compass, gps • promises 5-10% error to distance • very sensitive to pre-defined step length • indoors problems with heading • IMU (inertial measurement unit, commercial) • three vibration gyroscopes • three silicon-chip accelerometers • SiLMU (Stride length measuring unit, lab-made) • US based ankle distance meter • 30Hz continuous measurement • To be developed in the project Jussi Suomela

  8. Dead reckoning, 1st tests • Unaccurate heading data  accumulating position error • Distance accuracy of DRM is depending on the preset step length  accumulating odometry error •  dead reckoning has to be supported Jussi Suomela

  9. Beacons on robots or dropped by the robots Observer unit measures distances to beacons Radio and US based low cost beacon system Simple system for small distances (<10m) PeNa: Robot aided localization Jussi Suomela

  10. Human SLAM • To improve the dead-reckoning • To provide accurate measurement data • The first feasibility tests with Sick-scanner and IMU (heading only) • Main problems are swinging and placement in human body, especially floor and ceiling echoes are a problem • In future more compact scanners will be available Jussi Suomela

  11. Human SLAM, feasibility tests • Raw laser data and position estimate (integrated from IMU) as inputs • Matching between scans with ”particle type algorithm” • Nearest neighbour method for each particle to find out the best match • Scan to map matching by optimizing point distances to lines (geometric map) Jussi Suomela

  12. Scan to scan Position of a human entity Global Map Map Matching Scanner based localization Comparison between • scan to scan matching • scan to map matching Heading estimation from gyroscope Jussi Suomela

  13. Human SLAM, 1st results • Map based localization succeeded • Human SLAM seems feasible • Used algorithm is non recoverable in case of failing • Other algorithms (probabalistic approach) and tests in more difficult conditions are under work Jussi Suomela

  14. Robot Entities • Controlled by the operator • Mapping continuously the environment • Sending the mapping and visual information to operator • Carry and/or drop localization beacons Jussi Suomela

  15. Building Presence • Model(SRM) + database • Static structures included in model • Objects in database • Geometric polygon model • For robots geometric map is converted to OCM • For Humans geometric model is visualized • Final updating by operator Jussi Suomela

  16. Conclusions • Cooperative mapping of environment with robot and human entities • Project in the start phase • All methods will be developed, tested, integrated and demonstrated before end of 2004 • Methods (partially) tested until now: • Human dead reckoning • Feasibility of human slam • 3D modelling with augmented data • Stride length measurements Jussi Suomela

  17. Human SLAM demo Jussi Suomela

  18. Standard Resque Map (SRM) • SRM is the apriori map from the environment (2 or 3D) • ECDIS type multi- layer structure • Model(map) + database • Structure in model • Objects in database • Alarms and alarm areas • Sprinklers • Dangerous materials • Etc. Jussi Suomela

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