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The APL was one of the first laboratories to own a laser range finder. A laser range finder is a non-contact device that can measure the shape of a surface in 3D.

In collaboration with the NRC (National Research Center), a sophisticated synchronized laser scanner, with precision of up to 0.1 mm, was developed. Over the years, the technology has become more accessible and the APL has developed its own custom design low-cost range finder.

The right image shows an example of a range image produced by the APL range finder. The sensor can sample roughly every millimeter squared of surface within a precision of 0.5 mm. While the quality of this raw data is very good, these images are still numbers to the computer and require powerful computer vision algorithms to be interpreted efficiently and automatically.
A useful step in range image reconstruction has to do with the computation of the surface curvature to remove measurement noise and expose the underlying surface. The computation of the surface curvature, related to the second derivative of the surface, is very sensitive to noise. A method called "curvature consistency" propagates the information from neighbor to neighbor in order to achieve a stable and reliable estimation of the curvature. The picture shows a color-coded mean and gaussian curvature map found with this algorithm.
The analysis of range images starts often by their segmentation. Range image segmentation consists in grouping together the pixels of similar properties. In our case, we try to group the pixels according to the large structures of the object. The right image shows a color coding of the various parts found automatically on a small owl statue. 
Range images can contain many thousands of points. In order to make them more managable, geometric models are fitted to the various parts found by the segmentation algorithm. This results in a considerable data reduction and the model obtained can be used efficiently in order to get the robot to grasp objects, avoid collisions and recognize objects. The colors in the left image represent the degree of confidence in the model.
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