  Visual Place Recognition (VPR) is often characterized as being able to
recognize the same place despite significant changes in appearance and
viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling
robotic platforms and intelligent augmentation platforms such as augmented
reality devices to perceive and understand the physical world. In this paper,
we observe that there are three "drivers" that impose requirements on spatially
intelligent agents and thus VPR systems: 1) the particular agent including its
sensors and computational resources, 2) the operating environment of this
agent, and 3) the specific task that the artificial agent carries out. In this
paper, we characterize and survey key works in the VPR area considering those
drivers, including their place representation and place matching choices. We
also provide a new definition of VPR based on the visual overlap -- akin to
spatial view cells in the brain -- that enables us to find similarities and
differences to other research areas in the robotics and computer vision fields.
We identify numerous open challenges and suggest areas that require more
in-depth attention in future works.
