$\pm$0.1mm accuracy. We first perform a once-off hand-eye calibration step to find the transformation from the end-effector to the webcam using Tsai and Lenz’s method [@Tsai1989]. We then collected a simple image sequence using the webcam.

We create a metric 3D map from two pre-selected view points using the ground-truth poses. For each view point, a Symbolic Linear Predictor is learned for each observation corresponding to a 3D landmark. For each subsequent frame in the image sequence, we use the front-end of the SLAM system of [@Dharmasiri2016] to localize the camera relative to the map. However, instead of using the subpixel refinement technique in [@Dharmasiri2016] whereby only the translational parameters and mean intensity difference are optimized, we use our proposed method that handles the full affine motion model.

We measured the absolute trajectory error (ATE) [@Sturm2012] by running two variants of the experiment: (1) with and without subpixel refinement using all the keypoints observed by the current view, and (2) only using the best and worst 250 observed keypoints as predicted by the metric (\[eq:expected\_sq\_err\]). The result is shown in Table \[tab:localization\_results\]. The best ATE is produced when subpixel refinement is performed. Using the best 250 observed keypoints produces a 