Robust Subspace Position Measurement Using Localized Sub-Windows

The use of localized principal component analysis is examined for visual position determination in the presence of varying degrees of occlusions. Occlusions lead to substantial position measurement errors when projecting images into eigenspace. One way to improve robustness to occlusions is to select small sub-windows so that if some sub-windows are occluded, others can still accurately identify position. The location of candidate sub-windows are predetermined from a set of training images by subtracting the average image from each and then selecting regions using an attention operator. Since attention operators can be computationally time-intensive, the location of all sub-windows are determined a-priori during the training phase. The sub-windows in each of the training images are then projected into eigenspace. Once the training phase is complete, the run-time execution can be performed efficiently since all the sub-windows have been preselected. Input images are classified by each sub-window; majority voting is then used to determine the position estimate. Various experiments are performed including linear and rotational motion, and the ego motion of a mobile robot. This technique is shown to provide greater position measurement accuracy in the presence of severe occlusions as compared to the projection of entire images.