Robot vision lesson 1a structured light 3d reconstruction matthias r ther christian reinbacher
Sponsored Links
This presentation is the property of its rightful owner.
1 / 18

ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher PowerPoint PPT Presentation


  • 99 Views
  • Uploaded on
  • Presentation posted in: General

ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher. Structured Light Methods. Goal: Robust 3D Reconstruction through triangulation Project artificial pattern on the object Pattern alleviates the correspondence problem Variants:

Download Presentation

ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


ROBOT VISIONLesson 1a: Structured Light 3D ReconstructionMatthias Rüther, Christian Reinbacher


Structured Light Methods

  • Goal: Robust 3D Reconstruction through triangulation

  • Project artificial pattern on the object

  • Pattern alleviates the correspondence problem

  • Variants:

    • Laser Pattern (point, line)

    • Structured projector pattern (several lines, pattern sequence)

    • Random projector pattern


Structured Light Range Finder

1. Sender (projects plane)

2. Receiver (CCD Camera)

Sensor image

Geometry Z- direction

X- direction


1 plane -> 1 object profile

  • To get a 3D profile:

    • Move the object

    • Scanning Unit for projected plane

    • Move the Sensor

Object motion by conveyor band:

=> synchronization: measure distance along conveyor

=> y-accuracy determined by distance measurement

Scanning Units (e.g.: rotating mirror) are rare (accurate measurement of mirror motion is hard, small inaccuracy there -> large inaccuracy in geometry

Move the sensor: e.g. railways: sensor in wagon coupled to speed measurement


Commercially Available

Person Scanners

Cultural Heritage

Rapid Prototyping


Problems of Laser Profile

  • Occlusions:

    Object points need to be seen from Laser and Camera viewpoint

  • Sharpness and Contrast:

    Both camera and laser need to be in focus

  • Speckle noise:

    Laser always shows “speckle noise”, caused by interference of coherent light.

    -> where is the center of the stripe?


Multiple Sheets of Light

Project multiple Laser planes simultaneously to reduce measurement time.

Problem:

Separation of stripes in the image

Application:

Smoothness check of flat surfaces


Pattern projection

Range Image

Projected light stripes

  • Camera: IMAG CCD, Res:750x590, f:16 mm

  • Projector: Liquid Crystal Display (LCD 640), f: 200mm, Distance to object plane: 120cm


Projector

Lamp

Lens system

LCD - Shutter

Pattern structure

Line projector (e.g.: LCD-640)

Focusing lens (e.g.: 150mm)

Example


Depth decoding

Project Temporal sequenceof n binarymasks. Ateachpixel, the temporal sequenceofintensities (I1, …, In) gives a binarynumberwhichdenotedthecorrespondingprojectorcolumn.

Project  Acquire  Decode  Triangulate


Coded Light + Phase Shift

Binary code is limited to pixel accuracy (or less).

Increase accuracy to sub-pixel by projecting sine wave after code and measuring phase shift between projected and captured pattern. Decode phase from four samples of sine period, shifted by pi/2.


Coded Light + Phase Shift

Increase accuracy to sub-pixel by projecting sine wave after code and measuring phase shift between projected and captured pattern. Decode phase from four samples of sine period, shifted by pi/2.

code

Image column (x)

phase

+

2

0

Image column (x)


Other Coding Methods Possible

Joaquim Salvi,

Pattern codification strategies in structured light systems


The Kinect Working Principle

  • Triangulation based depth sensor

  • Static pattern projection

  • Heavy exploitation of redundancy

  • Extremely robust/conservative depth maps


The Sensor System

IR Lens:

F~6mm FOV~55°

Diffractive Optical

Element (DOE)

Laser

830nm, 60mW

class 3B without optics, 1 with optics,

no amplitude modulation

IR Bandpass

RGB Lens:

F~2.9mm, FOV~65°

IR Camera:

CMOS, rolling shutter, 1.3MP, ½“, 10bit

RGB Camera:

CMOS, rolling shutter, 1.3MP, 1/4“, 10bit

Peltier Element

Temperature Stabilization

Stereo Processor

Microphone Array

Accelerometer

Tilt Axis


The Sensor System

  • Tx ~75mm

  • DOF 0.5m – 8m

  • FOV ~55°

  • Res. 640x480 (at most)

  • Internal max 1280x1024

Stereo Processor

Microphone Array

Accelerometer

Tilt Axis


The Projection Pattern

IR Laser and Diffractive Optical Element create interference pattern

Pattern is static and identical for all Kinects


  • Login