  In this study, we address the problem of supervised change detection for
robotic map learning applications, in which the aim is to train a
place-specific change classifier (e.g., support vector machine (SVM)) to
predict changes from a robot's view image. An open question is the manner in
which to partition a robot's workspace into places (e.g., SVMs) to maximize the
overall performance of change classifiers. This is a chicken-or-egg problem: if
we have a well-trained change classifier, partitioning the robot's workspace
into places is rather easy. However, training a change classifier requires a
set of place-specific training data. In this study, we address this novel
problem, which we term unsupervised place discovery. In addition, we present a
solution powered by convolutional-feature-based visual place recognition, and
validate our approach by applying it to two place-specific change classifiers,
namely, nuisance and anomaly predictors.
