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Visualization of simultaneous localization and mapping using svg - ICISBC 2013

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  • Visualization of Simultaneous

    Localization and Mapping using SVG

    Harindra W Pradhana, Suryono, Achmad Widodo

    [email protected], [email protected], [email protected]

    mailto:[email protected]:[email protected]:[email protected]

  • Content

    Introduction

    SLAM

    Backgrounds & Objectives

    Data Preparation

    Movement and Detection Recap

    Movement Recap

    Detection Recap

    Map Generation

    Data Composition

    Map Plotting

    Conclusion

  • Simultaneous

    Localization and

    Mapping (SLAM)

    SLAM Problems : Posibilities

    of generating map and

    simultaneously determining

    locations of mobile robot

    dropped at unknown

    location in unknown

    environment (Durrant-

    Whyte and Bailey, 2006)

    First announced on IEEE

    Robotics and Automation

    Conference in 1986

  • Backgrounds & Objectives

    Backgrounds Objectives

    Why SLAM?

    SLAM problem already

    solved but still many area of

    development

    Most research focusing on

    efficiency and precission

    Contain complex data

    carry various information

    Why SVG?

    Simplicity & compatibility

    Building information

    system that are

    Provide easily

    understandable visualization

    Adaptable to SLAM system

    Using non destructive

    observation

    Capable to check data

    consistency

  • Data Preparations

    Extraction

    System log, db queries, etc

    Read only, no changed commited to system

    Unit conversion

    Metric system

    Polar coordinate system

    Cleaning

    System message, redundant record, etc

    Consistency check

    Continous data stream generate single map, otherwise

    generate new map

  • Movement Recap

    Continuous Discrete

    Each step represented as

    vector

    Each movement vector

    start at the end of previous

    vector

    l n=(x=1

    x=n

    l xsin x)2

    +(x=1

    x=n

    l xcos x)2

    n=tan1

    (x=1

    x=n

    l x sin x )

    (x=1

    x=n

    l x cos x)

    n=x=1

    x=n

    x

  • Detection Recap

    Relatively observed by

    robot

    Estimate object location

    on the map based on

    robot estimated position

    and orientation

    lm=( l ncosn+lmn cos( n+mn))2+( l nsinn+lmnsin ( n+mn))

    2

    m=tan1 [ ln sinn+lmn sin(n+mn)]

    [lncosn+lmn cos( n+mn)]

  • Map Generation

    Data Composition Simulation Data

    Map matrix X contain agent Aand objects O X = [ A O ]

    Agent matrix A contain historical position An A = [ A1 A2 A3 An ]

    An = [ ln n n ]

    Object matrix O contain every object Om on the map O = [ O1 O2 O3 Om ]

    Object Om contain estimated location and detection history Om = [ lm m Dm ]

  • Plotting Simulation 1

    Detection Map Spring Map

  • Plotting Simulation 2

    Detection Map Spring Map

  • Plotting Simulation 3

    Detection Map Spring Map

  • Plotting Simulation 4

    Detection Map Spring Map

  • Conclusions & Suggestions

    Conclusions Sugestions

    SLAM data can be

    visualized using XML tags

    in SVG form

    Data composition

    required to standardize

    data for visualization

    Recapitulation can use

    vector addition principles

    3D map

    Map feature

    Zoom

    Rotate

    Multiple robot

    Submap consolidation

    Particle Filtering

  • Thank You