  Convolutional Neural Networks (CNNs) have recently been shown to excel at
performing visual place recognition under changing appearance and viewpoint.
Previously, place recognition has been improved by intelligently selecting
relevant spatial keypoints within a convolutional layer and also by selecting
the optimal layer to use. Rather than extracting features out of a particular
layer, or a particular set of spatial keypoints within a layer, we propose the
extraction of features using a subset of the channel dimensionality within a
layer. Each feature map learns to encode a different set of weights that
activate for different visual features within the set of training images. We
propose a method of calibrating a CNN-based visual place recognition system,
which selects the subset of feature maps that best encodes the visual features
that are consistent between two different appearances of the same location.
Using just 50 calibration images, all collected at the beginning of the current
environment, we demonstrate a significant and consistent recognition
improvement across multiple layers for two different neural networks. We
evaluate our proposal on three datasets with different types of appearance
changes - afternoon to morning, winter to summer and night to day.
Additionally, the dimensionality reduction approach improves the computational
processing speed of the recognition system.
