Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover, Hans Moravec, 1980
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## Chapter 7: Stereo

### Slider Stereo

At each pause on its computer controlled itinerary the cart slides its camera from left to right on the 52 cm track, taking 9 pictures at precise 6.5 cm intervals.

Points are chosen in the fifth (middle) of these 9 images, either by the correlator to match features from previous positions, or by the interest operator. Figure 7.1: A typical ranging. The nine pictures are from a slider scan. The interest operator chose the marked feature in the central image, and the correlator found it in the other eight. The small curves at bottom are distance measurements of the feature made from pairs of the images. The large beaded curve is the sum of the measurements over all 36 pairings. The horizontal scale is linear in inverse distance. Figure 7.2: Ranging a distant feature Figure 7.3: Ranging in the presence of a correlation error. Note the mis-match in the last image. Correct feature pairs accumulate probability at the correct distance, while pairs with the incorrect feature dissipate their probability over a spread of distances.

The camera slides parallel to the horizontal axis of the (distortion corrected) camera co-ordinate system, so the parallax-induced apparent displacement of features from frame to frame in the 9 pictures is purely in the X direction.

The correlator looks for the points chosen in the central image in each of the eight other pictures. The search is restricted to a narrow horizontal band. This has little effect on the computation time, but it reduces the probability of incorrect matches.

In the case of correct matches, the distance to the feature is inversely proportional to its displacement from one image to another. The uncertainty in such a measurement is the difference in distance a shift one pixel in the image would make. The uncertainty varies inversely with the physical separation of the camera positions where the pictures were taken (the stereo baseline). Long baselines give more accurate distance measurements..

After the correlation step the program knows a feature's position in nine images. It considers each of the 36 ($={9 \choose 2}$) possible image pairings as a stereo baseline, and records the estimated distance to the feature (actually inverse distance) in a histogram. Each measurement adds a little normal curve to the histogram, with mean at the estimated distance, and standard deviation inversely proportional to the baseline, reflecting the uncertainty. The area under the each curve is made proportional to the product of the correlation coefficients of the matches in the two images (in central image this coefficient is taken as unity), reflecting the confidence that the correlations were correct. The area is also scaled by the normalized dot products of X axis and the shift of the features in each of the two baseline images from the central image. That is, a distance measurement is penalized if there is significant motion of the feature in the Y direction.

The distance to the feature is indicated by the largest peak in the resulting histogram, if this peak is above a certain threshold. If below, the feature is forgotten about.

The correlator frequently matches features incorrectly. The distance measurements from incorrect matches in different pictures are usually inconsistent. When the normal curves from 36 pictures pairs are added up, the correct matches agree with each other, and build up a large peak in the histogram, while incorrect matches spread themselves more thinly. Two or three correct correlations out of the eight will usually build a peak sufficient to offset a larger number of errors.

In this way eight applications of a mildly reliable operator interact to make a very reliable distance measurement. Figures 7-1 through 7-3 show typical rangings. The small curves are measurements from individual picture pairs, the beaded curve is the final histogram.

### Motion Stereo

The cart navigates exclusively by vision. It deduces its own motion from the apparent 3D shift of the features around it.

After having determined the 3D location of objects at one position, the computer drives the cart about a meter forward.

At the new position it slides the camera and takes nine pictures. The correlator is applied in an attempt to find all the features successfully located at the previous position. Feature descriptions extracted from the central image at the last position are searched for in the central image at the new stopping place.

Slider stereo then determines the distance of the features so found from the cart's new position. The program now knows the 3D position of the features relative to its camera at the old and the new locations. It can figure out its own movement by finding the 3D co-ordinate transform that relates the two.

There can be mis-matches in the correlations between the central images at two positions and, in spite of the eight way redundancy, the slider distance measurements are sometimes in error. Before the cart motion is deduced, the feature positions are checked for consistency. Although it doesn't yet have the co-ordinate transform between the old and new camera systems, the program knows the distance between pairs of positions should be the same in both. It makes a matrix in which element $[i,j]$ is the absolute value of the difference in distances between points $i$ and $j$ in the first and second co-ordinate systems divided by the expected error (based on the one pixel uncertainty of the ranging). Figure 7.4: The feature list before and after the mutual-distance pruning step. In this diagram the boxes represent features whose three dimensional position is known. Figure 7.5: Another pruning example, in more difficult circumstances. Sometimes the pruning removed too many points. The cart collided with the cardboard tree to the left later in this run.

Each row of this matrix is summed, giving an indication of how much each point disagrees with the other points. The idea is that while points in error disagree with virtually all points, correct positions agree with all the other correct ones, and disagree only with the bad ones.

The worst point is deleted, and its effect is removed from the remaining points in the row sums. This pruning is repeated until the worst error is within the error expected from the ranging uncertainty.

After the pruning, the program has a number of points, typically 10 to 20, whose position error is small and pretty well known. The program trusts these, and records them in its world model, unless it had already done so at a previous position. The pruned points are forgotten forevermore.

Now comes the co-ordinate transform determining step. We need to find a three dimensional rotation and translation that, if applied to the co-ordinates of the features at the first position, minimizes the sum of the squares of the distances between the transformed first co-ordinates and the raw co-ordinates of the corresponding points at the second position. Actually the quantity that's minimized is the foregoing sum, but with each term divided by the square of the uncertainty in the 3D position of the points involved, as deduced from the one pixel shift rule. This weighting does not make the solution more difficult.

The error expression is expanded. It becomes a function of the rotation and translation, with parameters that are the weighted averages of the $x$, $y$ and $z$ co-ordinates of the features at the two positions, and averages of their various cross-products. These averages need to be determined only once, at the begining of the transform finding process.

To minimize the error expression, its partial derivative with respect to each variable is set to zero. It is relatively easy to simultaneously solve the three linear equations thus resulting from the vector offset, getting the optimal offset values for a general rotation. This gives symbolic expressions (linear combinations of the rotation matrix coefficients) for each of the three vector components. Substituting these values into the error expression makes it a function of the rotation alone. This new, translation determined, error expression is used in all the subsequent steps.

Minimizing the error expression under rotation is surprisingly difficult, mainly because of the non-linear constraints in the 3D rotation matrix. The next six paragraphs outline the struggle. Each step was forced by the inadequacies of the previous one.

The program begins by ignoring the non-linearities. It solves for the general 3D linear transformation, nine elements of a matrix, that minimizes the least square error. The derivatives of the error expression with respect to each of the matrix coefficients are equated to zero, and the nine resulting simultaneous linear equations are solved for the nine coefficients. If the points had undergone an error-free rigid rotation and translation between the two positions, the result would be the desired rotation matrix, and the problem would be solved.

Because there are errors in the determined position of the features, the resulting matrix is usually not simply a rotation, but involves stretching and skewing. The program ortho-normalizes the matrix. If the position errors were sufficiently small, this new matrix would be our answer.

The errors are high enough to warrant adding the rigid rotation constraints in the least squares minimization. The error expression is converted from a linear expression in nine matrix coefficients into an unavoidably non-linear function in three parameters that uniquely characterize a rotation.

This new error expression is differentiated with respect to each of the three rotation parameters, and the resulting expressions are equated to zero, giving us three non-linear equations in three unknowns. A strenuous attempt at an analytic solution of this simultaneous non-linear system failed, so the program contains code to solve the problem iteratively, by Newton's method.

The rotation expressed by the ortho-normalized matrix from the previous step becomes the initial approximation. Newton's method for a multi-variate system involves finding the partial derivative of each expression whose root is sought with respect to each variable. In our case there are three variables and three equations, and consequently nine such derivatives. The nine derivatives, each a closed form expression of the rotation variables, are the coefficients of a 3 by 3 covariance matrix that characterizes the first order changes in the expressions whose roots are sought with the parameters. The next Newton's method approximation is found by multiplying the inverse of this matrix by the value of the root expressions, and subtracting the resulting values (which will be 0 at the root) from the parameter values of the previous approximation.

Four or five iterations usually brings the parameters to within our floating point accuracy of the correct values. Occasionally, when the errors in the determined feature locations are high, the process does not converge. The program detects this by noting the change in the original error expression from iteration to iteration. In case of non-convergence, the program picks a random rotation as a new starting point, and tries again. It is willing to try up to several hundred times. The rotation with the smallest error expression ever encountered during such a search (including the initial approximation) is returned as the answer.

Since the summations over the co-ordinate cross-products are done once and for all at the begining of the transformation determination, each iteration, involving evaluation of about a dozen moderately large expressions and a 3 by 3 matrix inversion, is relatively fast. The whole solving process, even in cases of pathological non-convergence, takes one or two seconds of computer time.

Appendix 7 presents the mathematics of the transform finder in greater detail.

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