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Co-operative localization and Mapping of Autonomous Robots

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

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Co-operative localization and Mapping of Autonomous Robots

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

  2. Presentation overview • Introduction • SLAM • CLAM • History and Background • Hardware • Localization Algorithms • Map Merging • Project Implementation

  3. introduction • Co-operative Localization and Mapping (CLAM) • 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. Slam framework overview

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

  7. 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

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

  9. Hardware: addons Motor Encoders Ultrasonic Sensors

  10. Triangular based fusion Sonar Wide Scan Arc TBF

  11. Random sample consensus (ransac) • General parameter estimation approach designed to cope with a large proportion of outliers in the input data. • Resampling technique that generates candidate solutions by using the minimum number of observations required to estimate the underlying model parameters. • I will be using the least-squares regression model as the underlying model • RANSAC uses the smallest set possible and proceeds to enlarge this set with consistent data points • Unlike conventional sampling techniques that use as much of the data as possible to obtain an initial solution and prune outliers

  12. Example range scan

  13. Least Squares Approximation

  14. Ransac Least Squares approximation

  15. Localization algorithms • Assumptions: • Unique Landmark Associations and adequately spaced landmarks • Time between observations • Static Environment • One robot will be used to avoid dealing with robot detection • The Algorithms • Extended Kalman Filter • Monte Carlo Particle Filter

  16. Map Building • Occupancy Grid Maps • Topological Maps Robots assumed to have compass to aid with map orientation!

  17. Grid Maps

  18. Grid Map data points

  19. Occupancy Grid maps

  20. Grid Map Data Points with Ransac

  21. Ransac Occupancy grid map

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

  23. Project implementation • XBoxUtils (Using pygame, zmq) • DatabaseUtils (Using sqlite3) • RansacUtils • MapBuildUtils • MapMergeUtils

  24. Questions?

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