ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher Kawada Industries Inc. has introduced the HRP-2P for Robodex 2002. This humanoid appears to be very impressive. It is 154 cm (60") tall, weighs 58kg (127 lbs) and has 30 DOF. Here is a news release. Notice the LACK of a battery pack. Here is a new story about HRP2.
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) Geometry Z- direction X- direction Sensor image
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 1) peak at position xo 2) left side und right side of the peal using a threshold Vt 3) center points
Pattern projection Camera: IMAG CCD, Res:750x590, f:16 mm Projected light stripes Camera: IMAG CCD, Res:750x590, f:16 mm Projector: Liquid Crystal Display (LCD 640), f: 200mm, Distance to object plane: 120cm Range Image
Line projector (e.g.: LCD-640) Lamp Lens system LCD - Shutter Pattern structure Line projector (e.g.: LCD-640) Focusing lens (e.g.: 150mm) Example
Project Acquire Decode Triangulate 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
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 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 Accelerometer Microphone Array Tilt Axis Stereo Processor
The Projection Pattern IR Laser and Diffractive Optical Element create interference pattern Pattern is static and identical for all Kinects