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Difference between revisions of "Imaging Information"

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== RGB Camera ==
 
== RGB Camera ==
  
The RGB camera has a slightly larger angle of view than the Depth camera
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The RGB camera has a slightly larger angle of view than the Depth camera. For computer vision applications, tt can be calibrated using standard techniques, e.g from [http://opencv.willowgarage.com/documentation/cpp/camera_calibration_and_3d_reconstruction.html OpenCV].
  
 
== Depth Camera ==
 
== Depth Camera ==
  
Lots of information on calibrating the depth camera is available on the [http://www.ros.org/wiki/kinect_node ROS kinect_node page]
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Lots of information on calibrating the depth camera is available on the [http://www.ros.org/wiki/kinect_node ROS kinect_node page].
  
 
From their data, a basic first order approximation for converting the raw 11-bit disparity value to a depth value in centimeters is: 100/(-0.00307 * rawDisparity + 3.33). This approximation is approximately 10 cm off at 4 m away, and less than 2 cm off within 2.5 m. A more dense set of data and second or third order approximation could increase the accuracy maybe by an order of magnitude.
 
From their data, a basic first order approximation for converting the raw 11-bit disparity value to a depth value in centimeters is: 100/(-0.00307 * rawDisparity + 3.33). This approximation is approximately 10 cm off at 4 m away, and less than 2 cm off within 2.5 m. A more dense set of data and second or third order approximation could increase the accuracy maybe by an order of magnitude.
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To convert the 11-bit disparity value to an 8-bit grayscale value that is fairly linear with respect to distance: (2048 * 256) / (2048 - rawDisparity). Also, background noise can be effectively eliminated by ignoring rawDisparity values above 1023.
 
To convert the 11-bit disparity value to an 8-bit grayscale value that is fairly linear with respect to distance: (2048 * 256) / (2048 - rawDisparity). Also, background noise can be effectively eliminated by ignoring rawDisparity values above 1023.
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== Color/Depth Mapping ==
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To enable accurate mapping between depth pixels and color pixels and obtain colored point clouds, the intrinsics parameters of both depth and color cameras are required (focal distances, distortion coefficients and image center), but their relative position and orientation in the world coordinate frame also need to be estimated. A preliminary attempt to extract all these parameters in a semi-automatic way is described there [http://nicolas.burrus.name/index.php/Research/Kinect].

Revision as of 00:02, 21 November 2010

RGB Camera

The RGB camera has a slightly larger angle of view than the Depth camera. For computer vision applications, tt can be calibrated using standard techniques, e.g from OpenCV.

Depth Camera

Lots of information on calibrating the depth camera is available on the ROS kinect_node page.

From their data, a basic first order approximation for converting the raw 11-bit disparity value to a depth value in centimeters is: 100/(-0.00307 * rawDisparity + 3.33). This approximation is approximately 10 cm off at 4 m away, and less than 2 cm off within 2.5 m. A more dense set of data and second or third order approximation could increase the accuracy maybe by an order of magnitude.

Once you have the distance using the measurement above A good approximation for converting (i, j, z) to (x,y,z) is: x = (i - w / 2) * (z + minDistance) * scaleFactor y = (j - h / 2) * (z + minDistance) * scaleFactor z = z

Where minDistance = -10 and scaleFactor = .0021. These values were found by hand.

To convert the 11-bit disparity value to an 8-bit grayscale value that is fairly linear with respect to distance: (2048 * 256) / (2048 - rawDisparity). Also, background noise can be effectively eliminated by ignoring rawDisparity values above 1023.

Color/Depth Mapping

To enable accurate mapping between depth pixels and color pixels and obtain colored point clouds, the intrinsics parameters of both depth and color cameras are required (focal distances, distortion coefficients and image center), but their relative position and orientation in the world coordinate frame also need to be estimated. A preliminary attempt to extract all these parameters in a semi-automatic way is described there [1].