Humans localize themselves efficiently in known environments by first
recognizing landmarks defined on certain objects and their spatial
relationships, and then verifying the location by aligning detailed structures
of recognized objects with those in the memory. Inspired by this, we propose
the place recognition anywhere model (PRAM) to perform visual localization as
efficiently as humans do. PRAM consists of two main components - recognition
and registration. In detail, first of all, a self-supervised map-centric
landmark definition strategy is adopted, making places in either indoor or
outdoor scenes act as unique landmarks. Then, sparse keypoints extracted from
images, are utilized as the input to a transformer-based deep neural network
for landmark recognition; these keypoints enable PRAM to recognize hundreds of
landmarks with high time and memory efficiency. Keypoints along with recognized
landmark labels are further used for registration between query images and the
3D landmark map. Different from previous hierarchical methods, PRAM discards
global and local descriptors, and reduces over 90% storage. Since PRAM utilizes
recognition and landmark-wise verification to replace global reference search
and exhaustive matching respectively, it runs 2.4 times faster than prior
state-of-the-art approaches. Moreover, PRAM opens new directions for visual
localization including multi-modality localization, map-centric feature
learning, and hierarchical scene coordinate regression.

提出了一种地点识别模型 (PRAM)，通过识别和注册两个组件，基于自我监督的地标定义策略和基于转换器的深度神经网络，使得 PRAM 能够像人类一样有效地执行视觉定位。