Markless registration for scans of free form objects
Upcoming SlideShare
Loading in...5
×
 

Markless registration for scans of free form objects

on

  • 242 views

 

Statistics

Views

Total Views
242
Views on SlideShare
221
Embed Views
21

Actions

Likes
0
Downloads
2
Comments
0

2 Embeds 21

http://www.linkedin.com 18
https://www.linkedin.com 3

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Markless registration for scans of free form objects Markless registration for scans of free form objects Presentation Transcript

  • MARKLESS REGISTRATION FOR SCANS OF FREE-FORM OBJECTS
    Laboratory of photogrammetry of NTUAArtemis Valanis, PhD StudentCharalambos Ioannidis, Professor
  • Problem identification
    Target: to initialize the ICP algorithm
    in order to register partial scans
    of uniform or free-form objects
    Difficulty: no targets present
    no characteristic points identifiable
    in the area of overlap
  • Motivation
    Initial state
    Front view
    Side view
  • Motivation
    Result of ICP - no prior processing
    Front view
    Side view
  • Related Literature
    Various approaches for automatic ICP initialization:
    Bae & Lichti, 2004 Geometric primitives
    Gelfand, 2005 Feature points
    Hansen, 2006 Plane-matching
    Makadia, 2006 Extended Gaussian Images
    Biswas, 2006 Isosurfaces
  • Example Objects
    Bae & Lichti, 2004 Geometric primitives
    Gelfand, 2005 Feature points
    Hansen, 2006 Plane-matching
    Makadia, 2006 Extended Gaussian Images
    Biswas, 2006 Isosurfaces
  • Proposed approach
    Constrained acquisition process
    Properly adjusted methods that:
    Recover the relative transformation between two or more partial scans
    Approximately align the point clouds
    Enable the initialization of ICP
    Achieve the optimal alignment of partial scans without the use of targetsor the identification of conjugate points
  • Worked cases
  • Worked cases
  • Equipment used
    HDS2500
    FOV 40ox40o
    spot size = 6mm
    position accuracy = ±6mm (50m range)
  • Key Idea
    Y
    Y
    ω
    Z
    Z
    X
    X
    Y
    Z
    X
    Acquisition scenario
  • Key Idea
    Y
    Y
    Y
    Y
    Z
    ω
    ω
    X
    Z
    X
    Z
    Z
    X
    X
    Acquisition scenario
    Acquired data
    Proposed approach
  • Initial state
    Front view
    Side view
  • Result of ICP combined with the proposed method
    Front view
    Front view
    Side view
  • Proposed algorithm
    Data imported:
    2 scans acquired either by rotating the scan head vertically (ω angle) or horizontally (φ angle)
    Process:
    The space of the unknown parameter (ω or φ angle) is sequentially sampled in order to obtain an approximation of the unknown angle. If the value of the evaluated measure is minimized then an approximate value is derived
  • Sampling process 1/2
    If the unknown rotation is ω
    The ω is given an initial value 0 that is increased by 5g in every loop
    For every ω value, a rotation matrix is calculated and applied to the point-cloud that needs to be registered
    After the transformation, the area of overlap between the reference and the moving scan is calculated and a rectangular grid is defined
  • Sampling process 2/2
    The evaluated function i.e. the median of the distances of the two point clouds at the nodes of the grid along the Z direction, is derived based on 2D tesselations created for each point-cloud
    Once the comparison measure reaches a minimum, the process is repeated at the respective interval with a step of 1g
    When another minimum is detected, the final value is derived by a simple interpolation
  • Method Validation
    2 scans acquired by different ω angle
    5 targets used to evaluate the results
    Algorithm implemented in Matlab
    Calculation of the unknown transform in Cyclone and in Matlab
  • Initial State
  • Target distances as calculated for the original scans
  • Results of the sampling process
  • Results after the approximate alignment
  • Results after the approximate alignment
  • Result of ICP after the application of the proposed algorithm
  • Results of ICP after the application of the proposed algorithm
  • Application of the method for the monument of Zalongon
    9 set-ups
    14 scans in total
    4 scans with no tagets
    Back
    3 set-ups
    4 scans (2 single and a scan-pair)
    Front
    6 set-ups
    10 scans (3 single, 2 scan-pairs and a scan-triplet)
  • Accuracy evaluation for 2 scan-pairs
  • Accuracy evaluation for a scan-triplet
  • Registration results
  • 3D surface model
  • Merits of the proposed approach
    With minor modifications, it is as easily applied for horizontal rotations
    Applicable also for sequences of scans acquired under the described conditions
    Provides a solution in cases of serious space limitations
    A non-elaborate and effective solution for all of those who have invested on similar equipment
  • Thank you for your attention!