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Visual Servoing in ALIVE

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Auto Docking (Mechanical Attaching) Docking Panel. Agenda. ALIVE Overview (3) ... correspondence between the circle center (detected) in the image and a circle ... – PowerPoint PPT presentation

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Title: Visual Servoing in ALIVE


1
Visual Servoing in ALIVE
Qian Zhebin
2
Agenda
  • vALIVE Overview (3)
  • VDS Functional Specification (5)
  • VDS Algorithms (15)
  • Other Related Projects (7)

3
AUV ALIVE
  • ALIVE, Autonomous Light Intervention Vehicle
    (European Development Project)
  • First Intervention AUV Prototype
  • AUV, Autonomous Underwater Vehicle
  • To perform Workclass-ROV mission
  • ROV, Remotely Operated Vehicle

4
ALIVE Front-side View
5
ALIVEs Mission
  • Three Stages
  • Transit, Inertial Navigation System-based
  • To Safe Landing Zone (SLZ), 50-100m
  • Approach, Sonar-based
  • To 1.5-2m range from the docking panel
  • Docking, Video-based
  • To accurately grab the bars on the panel
  • Highlights
  • Cableless Tele-manipulation (with acoustic modem
    linking the Surface Control Unit)
  • Auto Docking (Mechanical Attaching)

6
Docking Panel
7
Agenda
  • ALIVE Overview (3)
  • v VDS Functional Specification (5)
  • VDS Algorithms (15)
  • Other Related Projects (7)

8
Inside ALIVE Subsystems
  • Inter-Module Connection is Ethernet, TCP/IP-based.

9
VDS
  • VDS, Visual Docking System
  • Interfacing with
  • Navigation
  • Sonar Docking System
  • Camera (which is as a independent component with
    device driver)
  • Vehicle Controller

10
VDS Input and Output
  • Messaging Framework
  • Messages between VDS and Others
  • Procedure without RESPONSE
  • Procedure with RESPONSE
  • Protocol Specification
  • Format
  • Semantics
  • Timing for response
  • Abnormal processing
  • Response time-out
  • Bad message element

11
Docking Scenario
  • MSC (Message Sequence Chart)
  • Note Finally to Hover at a Target Relative Pose
    for Docking

12
Key Messages
  • Input Current Image
  • Output VEHICLE POSE COMMAND (VDS?VC), containing
  • Discriminator
  • Message type
  • Six DoF of Relative Position between the Vehicle
    and the Panel
  • X
  • Y
  • Z
  • Pitch
  • Yaw
  • Roll

13
VDS Core
  • VDS Algorithm 3-D Pose Estimation
  • Engineering Requirements
  • Real-time
  • Configurable, environment varies
  • Robust

14
Agenda
  • ALIVE Overview (3)
  • VDS Functional Specification (5)
  • v VDS Algorithms (15)
  • Other Related Projects (7)

15
VDS Algorithm
  • Priori Knowns, from (re)configuration messages,
    or file (in algorithm test)
  • Panels 3-D Model, some Points, pi
  • Fixed focal length of Camera, f
  • Initial Pose, Rough, (t, R)
  • Algorithm Thresholds
  • Input Current Image, (u, v)
  • Output the Updated (t, R), translation and
    rotation

16
3-D Panel Model
  • Derived from Panel CAD
  • Three circle centers are selected to describe the
    model
  • Panel Coordinate, three points (px, py, pz)
  • pz 0

17
Current Image
18
Algorithm Steps
  • Canny Edge Detection (basis)
  • Contour Segmentation and Circle Detection with
    Center Calculated
  • Feature Point Correspondence between Model and
    Current Image
  • Pose Estimation (kernel)

19
Edge Detection
  • Canny Edge Detection
  • Input Current Image
  • Output Edges
  • Complex but essential and worthy

20
Circle Detection
  • Edge Contour Segmentation
  • Input binary edges
  • Output several contour objects, with the centers
    calculated
  • A stack is used to segment the continuous edges
    into contours
  • Circle Detection
  • Output 3 circle centers
  • According to the circle definition with some
    tolerances
  • The center history contexts used

21
Feature Correspondence
  • The correspondence between the circle center
    (detected) in the image and a circle point p on
    the model is established.
  • (ui, vi) ?? pi (pxi, pyi, pzi)
  • Note 2 outer control loops and stabilization
    module make the matching easy

22
Models Projection
  • !The task is to refine/solve for (t, R)
  • Nonlinear system
  • Note R is a matrix here, it has two equivalent
    forms in the vector and matrix.

23
Newtons Method
  • s is parameter vector, like (t, R). s0 can be
    very rough. e is the error between projective
    model and image.
  • In this case,

24
Partial Derivatives
25
Simulation and Tank Experiments
  • Image is formed with pin-hole projection plus
    some noise
  • Approaching procedure goes
  • Error between estimated and real converges
  • Accuracy Newtons Iteration
  • Intra-frame
  • Inter-frame

26
Conclusion
  • Single-view solution
  • Result Accuracy Newtons Iteration
  • Intra-frame
  • Inter-frame
  • Missions last meter

27
Conclusion
  • This single-view solution meets the requirements.
  • Last meters mission done

28
Agenda
  • ALIVE Overview (3)
  • VDS Functional Specification (5)
  • VDS Algorithms (15)
  • v Other Related Projects (7)

29
Other related work
  • Vehicle Stabilization with 2½D Visual Servoing
  • Use of 2-D Hopfield Neural Network for Image
    Registration
  • Point Target Detection from Low-SNR Sequences and
    Real-time Design
  • MPEG-4 CODEC and DSP Optimization

30
2½D Visual Servoing
31
Use of HNN for Feature Correspondence
  • Multiple Dimensional HNN and its Computational
    Complexity

32
Real-time Moving Point Target Detection
33
Multimedia Applications
  • Video MPEG-4 Codec
  • Codex Fitting onto TI C6x DSP
  • Memory Hierarchy
  • Program Optimization using Pipeline, VLIW, SIMD
  • Industry Standard System Platform
  • Device drivers
  • Linux, Windows, and DSP/BIOS

34
Imagination at work expected
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