Presentation is loading. Please wait.

Presentation is loading. Please wait.

Robot Vision SS 2013 Matthias Rüther 1 ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher.

Similar presentations


Presentation on theme: "Robot Vision SS 2013 Matthias Rüther 1 ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher."— Presentation transcript:

1 Robot Vision SS 2013 Matthias Rüther 1 ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher

2 Robot Vision SS 2013 Matthias Rüther 2 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

3 Robot Vision SS 2013 Matthias Rüther 3 Structured Light Range Finder 1. Sender (projects plane) 2. Receiver (CCD Camera) X- directionGeometry Z- direction Sensor image

4 Robot Vision SS 2013 Matthias Rüther 4 1 plane -> 1 object profile 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 To get a 3D profile: Move the object Scanning Unit for projected plane Move the Sensor

5 Robot Vision SS 2013 Matthias Rüther 5

6 6 Commercially Available Person Scanners Cultural Heritage Rapid Prototyping

7 Robot Vision SS 2013 Matthias Rüther 7 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?

8 Robot Vision SS 2013 Matthias Rüther 8 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

9 Robot Vision SS 2013 Matthias Rüther 9 Pattern projection Camera Camera: IMAG CCD, Res:750x590, f:16 mm Projector Projector: Liquid Crystal Display (LCD 640), f: 200mm, Distance to object plane: 120cm Projected light stripes Range Image

10 Robot Vision SS 2013 Matthias Rüther 10 Projector Lamp Lens system LCD - Shutter Pattern structure Example Focusing lens (e.g.: 150mm) Line projector (e.g.: LCD-640)

11 Robot Vision SS 2013 Matthias Rüther 11 Depth decoding Project Temporal sequence of n binary masks. At each pixel, the temporal sequence of intensities (I1, …, In) gives a binary number which denoted the corresponding projector column. Project  Acquire  Decode  Triangulate

12 Robot Vision SS 2013 Matthias Rüther 12 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.

13 Robot Vision SS 2013 Matthias Rüther 13 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. Image column (x) code Image column (x) phase + 0 22

14 Robot Vision SS 2013 Matthias Rüther 14 Other Coding Methods Possible Joaquim Salvi, Pattern codification strategies in structured light systems

15 Robot Vision SS 2013 Matthias Rüther 15 The Kinect Working Principle  Triangulation based depth sensor  Static pattern projection  Heavy exploitation of redundancy  Extremely robust/conservative depth maps

16 Robot Vision SS 2013 Matthias Rüther 16 The Sensor System IR Camera: CMOS, rolling shutter, 1.3MP, ½“, 10bit RGB Camera: CMOS, rolling shutter, 1.3MP, 1/4“, 10bit Accelerometer IR Bandpass IR Lens: F~6mm FOV~55° RGB Lens: F~2.9mm, FOV~65° Laser 830nm, 60mW class 3B without optics, 1 with optics, no amplitude modulation Diffractive Optical Element (DOE) Peltier Element Temperature Stabilization Microphone ArrayTilt AxisStereo Processor

17 Robot Vision SS 2013 Matthias Rüther 17 The Sensor System  T x ~75mm  DOF 0.5m – 8m  FOV ~55°  Res. 640x480 (at most)  Internal max 1280x1024 AccelerometerMicrophone ArrayTilt AxisStereo Processor

18 Robot Vision SS 2013 Matthias Rüther 18 The Projection Pattern IR Laser and Diffractive Optical Element create interference pattern Pattern is static and identical for all Kinects


Download ppt "Robot Vision SS 2013 Matthias Rüther 1 ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher."

Similar presentations


Ads by Google