co-operative mapping and localization of autonomous robots
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Principle Investigator: Lynton Dicks
Supervisor: Karen Bradshaw
CO-OPERATIVE MAPPING ANDLOCALIZATION OF AUTONOMOUS ROBOTS
• Introduction
• SLAM
• CSLAM
• History and Background
• Hardware
• Localization Algorithms
• Map Merging
PRESENTATION OVERVIEW
• Simultaneous Localization and Mapping (SLAM)
• Well researched for use on a single robot
• Uses:
• Google Autonomous Vehicles
• Navigate and map unreachable areas
• Military Reconnaissance
• Co-operative Mapping and Localization (CSLAM)
• Relatively new field
• Benefits:
• Team work saves time
• Improved Accuracy
INTRODUCTION
SIMULTANEOUS LOCALIZATION AND MAPPING
SLAM
State UpdateLandmark Tracking (Dead
reckoning)
Landmark Extraction
Data Association
Pose Tracking
Odometry
• Each robots role
• Master-slave
• Independent Entities
• Centralization / Convergence
• Aggregation
• Communication methods
COOPERATIVE MAPPING AND LOCALIZATION
• 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
HISTORY AND BACKGROUNDAutonomous Robotic Programming Framework – Leslie Luyt 2009
A Robotic Framework for use in Simultaneous Localization and Mapping Algorithms – Shaun Egan 2010
• Two Encoder Motors
• Two Ultrasonic Sensors
• A Bluetooth Controller – 10m range, ability to keep several connections alive at the same time
HARDWARE – FISCHERTECHNIK ROBOT
HARDWARE: ADDONS
Motor Encoders Ultrasonic Sensors
TRIANGULAR BASED FUSION
Sonar Wide Scan Arc TBF
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
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
Questions?