  Place recognition is a critical and challenging task for mobile robots,
aiming to retrieve an image captured at the same place as a query image from a
database. Existing methods tend to fail while robots move autonomously under
occlusion (e.g., car, bus, truck) and changing appearance (e.g., illumination
changes, seasonal variation). Because they encode the image into only one code,
entangling place features with appearance and occlusion features. To overcome
this limitation, we propose PROCA, an unsupervised approach to decompose the
image representation into three codes: a place code used as a descriptor to
retrieve images, an appearance code that captures appearance properties, and an
occlusion code that encodes occlusion content. Extensive experiments show that
our model outperforms the state-of-the-art methods. Our code and data are
available at https://github.com/rover-xingyu/PROCA.
