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Principle Investigator: Lynton Dicks Supervisor: Karen Bradshaw CO-OPERATIVE LOCALIZATION AND MAPPING OF AUTONOMOUS ROBOTS

Co-operative localization and Mapping of Autonomous Robots

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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. - PowerPoint PPT Presentation

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Page 1: Co-operative  localization and Mapping of  Autonomous Robots

Principle Investigator: Lynton Dicks

Supervisor: Karen Bradshaw

CO-OPERATIVE LOCALIZATION ANDMAPPING OF AUTONOMOUS ROBOTS

Page 2: Co-operative  localization and Mapping of  Autonomous Robots

• Introduction

• SLAM

• CLAM

• History and Background

• Hardware

• Localization Algorithms

• Map Merging

• Project Implementation

PRESENTATION OVERVIEW

Page 3: Co-operative  localization and Mapping of  Autonomous Robots

• 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 Localization and Mapping (CLAM)

• Relatively new field

• Benefits:

• Team work saves time

• Improved Accuracy

INTRODUCTION

Page 4: Co-operative  localization and Mapping of  Autonomous Robots

SIMULTANEOUS LOCALIZATION AND MAPPING

SLAM

State UpdateLandmark Tracking (Dead

reckoning)

Landmark Extraction

Data Association

Pose Tracking

Odometry

Page 5: Co-operative  localization and Mapping of  Autonomous Robots

SLAM FRAMEWORK OVERVIEW

Page 6: Co-operative  localization and Mapping of  Autonomous Robots

• Each robots role

• Master-slave

• Independent Entities

• Centralization / Convergence

• Aggregation

• Communication methods

COOPERATIVE LOCALIZATION AND MAPPING

Page 7: Co-operative  localization and Mapping of  Autonomous Robots

• 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

Page 8: Co-operative  localization and Mapping of  Autonomous Robots

• Two Encoder Motors

• Two Ultrasonic Sensors

• A Bluetooth Controller – 10m range, ability to keep several connections alive at the same time

HARDWARE – FISCHERTECHNIK ROBOT

Page 9: Co-operative  localization and Mapping of  Autonomous Robots

HARDWARE: ADDONS

Motor Encoders Ultrasonic Sensors

Page 10: Co-operative  localization and Mapping of  Autonomous Robots

TRIANGULAR BASED FUSION

Sonar Wide Scan Arc TBF

Page 11: Co-operative  localization and Mapping of  Autonomous Robots

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

Page 12: Co-operative  localization and Mapping of  Autonomous Robots

EXAMPLE RANGE SCAN

Page 13: Co-operative  localization and Mapping of  Autonomous Robots

LEAST SQUARES APPROXIMATION

Page 14: Co-operative  localization and Mapping of  Autonomous Robots

RANSAC LEAST SQUARES APPROXIMATION

Page 15: Co-operative  localization and Mapping of  Autonomous Robots

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

Page 16: Co-operative  localization and Mapping of  Autonomous Robots

MAP BUILDING

•Occupancy Grid Maps•Topological Maps

Robots assumed to have compass to aid with map orientation!

Page 17: Co-operative  localization and Mapping of  Autonomous Robots

GRID MAPS

Page 18: Co-operative  localization and Mapping of  Autonomous Robots

GRID MAP DATA POINTS

Page 19: Co-operative  localization and Mapping of  Autonomous Robots

OCCUPANCY GRID MAPS

Page 20: Co-operative  localization and Mapping of  Autonomous Robots

GRID MAP DATA POINTS WITH RANSAC

Page 21: Co-operative  localization and Mapping of  Autonomous Robots

RANSAC OCCUPANCY GRID MAP

Page 22: Co-operative  localization and Mapping of  Autonomous Robots

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

Page 23: Co-operative  localization and Mapping of  Autonomous Robots
Page 24: Co-operative  localization and Mapping of  Autonomous Robots
Page 25: Co-operative  localization and Mapping of  Autonomous Robots

PROJECT IMPLEMENTATION

•XBoxUtils (Using pygame, zmq)•DatabaseUtils (Using sqlite3)•RansacUtils•MapBuildUtils•MapMergeUtils

Page 26: Co-operative  localization and Mapping of  Autonomous Robots

Questions?