compute the average test error, corner points lying within the bounding box of the first image in the data set are computed. These points are projected onto the other images using the refined homographies. We quantize these projections to the nearest pixel location, thus creating $11-1=10$ point correspondences for every keypoint in the first image. For each keypoint, the average test error is computed using its point correspondences.

The results in Fig. \[fig:error\_prediction\](b) show scatter plots of the average predicted error against the average test error for the different planar targets. While the average test errors are generally higher than the average predicted errors, they exhibit a linear trend in general. This is expected as a margin of error is introduced in the ground-truth estimation process due to image noise, an imperfect camera model, and an imperfect alignment between the images.

Localization Experiment
-----------------------

![Localization experiment using UR5. (a) The experimental setup. (b) Estimated trajectory using the top 250 keypoints with the metric described in Sec. \[subsec:error\_prediction\].[]{data-label="fig:ur5_experiment"}](ur5_experiment.pdf){width="0.9\columnwidth"}

Finally, we evaluate our method in a simple localization setting shown in Fig. \[fig:ur5\_experiment\](a), where we have a webcam mounted on a 6DoF, UR5 robotic arm which provides accurate ground-truth poses up to 