  This paper proposes a novel point-cloud-based place recognition system that
adopts a deep learning approach for feature extraction. By using a
convolutional neural network pre-trained on color images to extract features
from a range image without fine-tuning on extra range images, significant
improvement has been observed when compared to using hand-crafted features. The
resulting system is illumination invariant, rotation invariant and robust
against moving objects that are unrelated to the place identity. Apart from the
system itself, we also bring to the community a new place recognition dataset
containing both point cloud and grayscale images covering a full $360^\circ$
environmental view. In addition, the dataset is organized in such a way that it
facilitates experimental validation with respect to rotation invariance or
robustness against unrelated moving objects separately.
