  Visual place recognition is essential for vision-based robot localization and
SLAM. Despite the tremendous progress made in recent years, place recognition
in changing environments remains challenging. A promising approach to cope with
appearance variations is to leverage high-level semantic features like objects
or place categories. In this paper, we propose FM-Loc which is a novel
image-based localization approach based on Foundation Models that uses the
Large Language Model GPT-3 in combination with the Visual-Language Model CLIP
to construct a semantic image descriptor that is robust to severe changes in
scene geometry and camera viewpoint. We deploy CLIP to detect objects in an
image, GPT-3 to suggest potential room labels based on the detected objects,
and CLIP again to propose the most likely location label. The object labels and
the scene label constitute an image descriptor that we use to calculate a
similarity score between the query and database images. We validate our
approach on real-world data that exhibit significant changes in camera
viewpoints and object placement between the database and query trajectories.
The experimental results demonstrate that our method is applicable to a wide
range of indoor scenarios without the need for training or fine-tuning.
