Unsupervised Feature Learning for Visual Place Recognition in Changing Environments

Visual place recognition in changing environments is a challenging and critical task for autonomous robot navigation. Deep convolutional neural networks (ConvNets) have recently been used as efficient feature extractors and obtained excellent performance in place recognition. However the success of Con-vNets' learning highly relies on the availability of large datasets with millions of labeled images, the collection of which is a tedious and costly burden. Thus we develop an unsupervised learning method (the siamese VisNet) to autonomously learn invariant features in changing environments from unlabeled images. The siamese VisNet has two identical branches of sub-networks. With a Hebbian-type of learning rule incorporating a trace of previous activity patterns, the siamese VisNet learns features with increasing invariance in changing environments from layer to layer. Experiments conducted on multiple datasets demonstrate the robustness of the siamese VisNet against viewpoint changes, appearance changes, and joint viewpoint-appearance changes. In addition, the siamese VisNet, with lower complexity in architecture, outperforms the state-of-the-art place recognition ConvNets such as the CaffeNet and the PlaceNet. The proposed siamese VisNet constitutes a biologically plausible yet efficient method for unsupervised place recognition