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Co-operative Mapping and Localization of Autonomous Robots

Co-operative Mapping and Localization of Autonomous Robots. Principle Investigator: Lynton Dicks Supervisor: Karen Bradshaw. Presentation overview. Introduction SLAM CSLAM History and Background Hardware Localization Algorithms Map Merging. introduction.

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Co-operative Mapping and Localization of Autonomous Robots

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  1. Co-operative Mapping andLocalization of Autonomous Robots Principle Investigator: Lynton Dicks Supervisor: Karen Bradshaw

  2. Presentation overview • Introduction • SLAM • CSLAM • History and Background • Hardware • Localization Algorithms • Map Merging

  3. introduction • Co-operative Mapping and Localization (CSLAM) • Relatively new field • Benefits: • Team work saves time • Improved Accuracy • Simultaneous Localization and Mapping (SLAM) • Well researched for use on a single robot • Uses: • Google Autonomous Vehicles • Navigate and map unreachable areas • Military Reconnaissance

  4. Simultaneous localization and mapping

  5. Cooperative mapping and localization • Each robots role • Master-slave • Independent Entities • Centralization / Convergence • Aggregation • Communication methods

  6. History and background Autonomous Robotic Programming Framework – Leslie Luyt 2009 A Robotic Framework for use in Simultaneous Localization and Mapping Algorithms – Shaun Egan 2010 • Generic Framework for both online and offline SLAM • Implemented SLAM for use with one robot • Generic Programming Framework to combine standard robotic operations with AI • Abstracts away the details of interfacing and controlling robots • Easy to implement new robot hardware classes to allow the framework to work with new hardware

  7. Hardware – Fischertechnik robot • Two Encoder Motors • Two Ultrasonic Sensors • A Bluetooth Controller – 10m range, ability to keep several connections alive at the same time

  8. Hardware: addons Motor Encoders Ultrasonic Sensors

  9. Triangular based fusion Sonar Wide Scan Arc TBF

  10. Localization algorithms • Constraints: • Unique Landmark Associations and adequately spaced landmarks • Time between observations • Static Environment • Limited to two robots • The Algorithms • Extended Kalman Filter • Monte Carlo Particle Filter

  11. Map merging • Merge maps with observed robot • Maps are transformed (rotated, translated) through merging algorithm • Merging maps of populated environments by keeping track of moving objects

  12. Questions?

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