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3 A New Method: Spacetime Analysis

The previous section clearly demonstrates that analyzing each imaged pulse using a low order statistic leads to systematic range errors. We have found that these errors can be reduced or eliminated by analyzing the time evolution of the pulses.

3.1 Geometric intuition

Figure 5 illustrates the principle of spacetime analysis for a laser triangulation scanner with Gaussian illuminant and orthographic sensor as it translates across the edge of an object. As the scanner steps to the right, the sensor images a smaller and smaller portion of the laser cross-section. By time tex2html_wrap_inline680 , the sensor no longer images the center of the illuminant, and conventional methods of range estimation fail. However, if we look along the lines of sight from the corner to the laser and from the corner to the sensor, we see that the profile of the laser is being imaged over time onto the sensor (indicated by the dotted Gaussian envelope). Thus, we can find the coordinates of the corner point tex2html_wrap_inline682 by searching for the mean of a Gaussian along a constant line of sight through the sensor images. We can express the coordinates of this mean as a time and a position on the sensor, where the time is in general between sensor frames and the position is between sensor pixels. The position on the sensor indicates a depth, and the time indicates the lateral position of the center of the illuminant. In the example of Figure 5, we find that the spacetime Gaussian corresponding to the exact corner has its mean at position tex2html_wrap_inline684 on the sensor at a time tex2html_wrap_inline686 between tex2html_wrap_inline688 and tex2html_wrap_inline680 during the scan. We extract the corner's depth by triangulating the center of the illuminant with the line of sight corresponding to the sensor coordinate tex2html_wrap_inline684 , while the corner's horizontal position is proportional to the time tex2html_wrap_inline686 .

Figure 5: Spacetime mapping of a Gaussian illuminant. As the light sweeps across the corner point, the sensor images the shape of the illuminant over time. 

3.2 A complete derivation

For a more rigorous analysis, we consider the time evolution of the irradiance from a translating differential surface element, tex2html_wrap_inline698 , as recorded at the sensor. We refer the reader to Figure 6 for a description of coordinate systems; note that in contrast to the previous section, the surface element is translating instead of the illuminant-sensor assembly.

Figure 6: Triangulation scanner coordinate system. A depiction of the coordinate systems and the vectors relevant to a moving differential element. 

The element has a normal tex2html_wrap_inline700 and an initial position tex2html_wrap_inline702 and is translating with velocity tex2html_wrap_inline704 , so that:


Our objective is to compute the coordinates tex2html_wrap_inline706 given the temporal irradiance variations on the sensor. For simplicity, we assume that tex2html_wrap_inline708 . The illuminant we consider is a laser with a unidirectional Gaussian radiance profile. We can describe the total radiance reflected from the element to the sensor as:


where tex2html_wrap_inline710 is the bidirectional reflection distribution function (BRDF) of the point tex2html_wrap_inline702 , tex2html_wrap_inline714 is the cosine of the angle between the surface and illumination. The remaining terms describe a point moving in the x-direction under the Gaussian illuminant of width w and power tex2html_wrap_inline720 .

Projecting the point tex2html_wrap_inline722 onto the sensor, we find:


where s is the position on the sensor and tex2html_wrap_inline654 is the angle between the sensor and laser directions. We combine Equations 2-3 to give us an equation for the irradiance observed at the sensor as a function of time and position on the sensor:


To simplify this expression, we condense the light reflection terms into one measure:


which we will refer to as the reflectance coefficient of point tex2html_wrap_inline728 for the given illumination and viewing directions. We also note that x=vt is a measure of the relative x-displacement of the point during a scan, and tex2html_wrap_inline734 is the relation between sensor coordinates and depth values along the center of the illuminant. Making these substitutions we have:


This equation describes a Gaussian running along a tilted line through the spacetime sensor plane or ``spacetime image''. We define the ``spacetime image'' to be the image whose columns are filled with sensor scanlines that evolve over time. Through the substitutions above, position within a column of this image represents displacement in depth, and position within a row represents time or displacement in lateral position. Figure 7 shows the theoretical spacetime image of a single point based on the derivation above, while Figures 8a and  8b shows the spacetime image generated during a real scan. From Figure 7, we see that the tilt angle is tex2html_wrap_inline736 with respect to the z-axis, and the width of the Gaussian along the line is:


The peak value of the Gaussian is tex2html_wrap_inline740 , and its mean along the line is located at tex2html_wrap_inline742 , the exact location of the range point. Note that the angle of the line and the width of the Gaussian are solely determined by the fixed parameters of the scanner, not the position, orientation, or BRDF of the surface element.

Figure 7: Spacetime image of a point passing through a Gaussian illuminant. 

Thus, extraction of range points should proceed by computing low order statistics along tilted lines through the sensor spacetime image, rather than along columns (scanlines) as in the conventional method. As a result, we can determine the position of the surface element independently of the orientation and BRDF of the element and independently of any other nearby surface elements. In theory, the decoupling of range determination from local shape and reflectance is complete. In practice, optical systems and sensors have filtering and sampling properties that limit the ability to resolve neighboring points. In Figure 8d, for instance, the extracted edges extend slightly beyond their actual bounds. We attribute this artifact to filtering which blurs the exact cutoffs of the edges into neighboring pixels in the spacetime image, causing us to find additional range values.



Figure 8: From geometry to spacetime image to range data. (a) The original geometry. (b) The resulting spacetime image. TA indicates the direction of traditional analysis, while SA is the direction of the spacetime analysis. The dotted line corresponds to the scanline generated at the instant shown in (a). (c) Range data after traditional mean analysis. (d) Range data after spacetime analysis.  

As a side effect of the spacetime analysis, the peak of the Gaussian yields the irradiance at the sensor due to the point. Thus, we automatically obtain an intensity image precisely registered to the range image.

3.3 Generalizing the geometry

We can easily generalize the previous results to other scanner geometries under the following conditions:

These conditions ensure that the reflectance coefficient, tex2html_wrap_inline750 , is constant. Note that the illumination need only be directional; coherent or incoherent light of any pattern is acceptable. Further, the translational motion need not be of constant speed, only constant direction; we can correct for known variations in speed by applying a suitable warp to the spacetime image.

We can weaken each of these restrictions if tex2html_wrap_inline752 does not vary appreciably for each point as it passes through the illuminant. A perspective sensor is suitable if the changes in viewing directions are relatively small for neighboring points inside the illuminant. This assumption of ``local orthography'' has yielded excellent results in practice. In addition, we can tolerate a rotational component to the motion as long as the radius of curvature of the point path is large relative to the beam width, again minimizing the effects on tex2html_wrap_inline752 .

3.4 Correcting laser speckle

The discussion in sections 3.1-3.3 show how we can go about extracting accurate range data in the presence of shape and reflectance variations, as well as occlusions. But what about laser speckle? Empirical observation of the time evolution of the speckle pattern with our optical triangulation scanner strongly suggests that the image of laser speckle moves as the surface moves. The streaks in the spacetime image of Figure 8b correspond to speckle noise, for the object has uniform reflectance and should result in a spacetime image with uniform peak amplitudes. These streaks are tilted precisely along the direction of the spacetime analysis, indicating that the speckle noise adheres to the surface of the object and behaves as a noisy reflectance variation. Other researchers have observed a ``stationary speckle'' phenomenon as well [1]. Proper analysis of this problem is an open question, likely to be resolved with the study of the governing equations of scalar diffraction theory for imaging of a rough translating surface under coherent Gaussian beam illumination [6].

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Next: 4 Implementation Up: Better Optical Triangulation through Previous: 2 Error in triangulation