Visual place recognition is an important tool for robots to localize themselves in their- surroundings by matching previously seen images. Recent methods based on Convolutional Neural Networks (CNN) are capable of successfully addressing the place recognition task in RGB-D images. However, these methods require many aligned and annotated intensity and depth images to train joint detectors. We propose a new approach by augmenting the place recognition process with individual separate intensity and depth networks trained on synthetic data. As a result, the new approach requires only a handful of aligned RGB-D frames to achieve a competitive place recognition performance. To our knowledge, this is the first CNN approach that integrates intensity and depth into a joint robust matching framework for place recognition and that evaluates utility of prediction from each modality.
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