Principle Investigator: Lynton Dicks

Supervisor: Karen Bradshaw

CO-OPERATIVE LOCALIZATION ANDMAPPING OF AUTONOMOUS ROBOTS

• Introduction

• SLAM

• CLAM

• History and Background

• Hardware

• Localization Algorithms

• Map Merging

• Project Implementation

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 Localization and Mapping (CLAM)

• 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

SLAM FRAMEWORK OVERVIEW

• Each robots role

• Master-slave

• Independent Entities

• Centralization / Convergence

• Aggregation

• Communication methods

COOPERATIVE LOCALIZATION AND MAPPING

• 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

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

EXAMPLE RANGE SCAN

LEAST SQUARES APPROXIMATION

RANSAC LEAST SQUARES APPROXIMATION

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

MAP BUILDING

•Occupancy Grid Maps•Topological Maps

Robots assumed to have compass to aid with map orientation!

GRID MAPS

GRID MAP DATA POINTS

OCCUPANCY GRID MAPS

GRID MAP DATA POINTS WITH RANSAC

RANSAC OCCUPANCY GRID MAP

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

PROJECT IMPLEMENTATION

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

Questions?