Unsupervised place discovery for visual place classification

Abstract

In this study, we explore the use of deep convolutional neural network (DCNN) in visual place classification for robotic mapping and localization. An open question is how to partition the robot's workspace into places so as to maximize the performance (e.g., accuracy, precision & recall) of potential DCNN classifiers. This is a chicken and egg problem: If we had a well-trained DCNN classifier, it is rather easy to partition the robot's workspace into places, but the training of a DCNN classifier requires a set of pre-defined place classes. In this study, we address this problem and present several strategies for unsupervised discovery of place classes (“time cue”, “location cue”, “time-appearance cue”, and “location-appearance cue”) and evaluate efficacy of the proposed methods using publicly available NCLT dataset.