  VPR is a fundamental task for autonomous navigation as it enables a robot to
localize itself in the workspace when a known location is detected. Although
accuracy is an essential requirement for a VPR technique, computational and
energy efficiency are not less important for real-world applications. CNN-based
techniques archive state-of-the-art VPR performance but are computationally
intensive and energy demanding. Binary neural networks (BNN) have been recently
proposed to address VPR efficiently. Although a typical BNN is an order of
magnitude more efficient than a CNN, its processing time and energy usage can
be further improved. In a typical BNN, the first convolution is not completely
binarized for the sake of accuracy. Consequently, the first layer is the
slowest network stage, requiring a large share of the entire computational
effort. This paper presents a class of BNNs for VPR that combines depthwise
separable factorization and binarization to replace the first convolutional
layer to improve computational and energy efficiency. Our best model achieves
state-of-the-art VPR performance while spending considerably less time and
energy to process an image than a BNN using a non-binary convolution as a first
stage.
