FM-Loc: Using Foundation Models for Improved Vision-Based Localization

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. Our approach 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.

Authors

• Reihaneh Mirjalili
• Michael Krawez
• Wolfram Burgard

Keywords

• Location awareness
• Training
• Visualization
• Simultaneous localization and mapping
• Databases
• Semantics
• Robot vision systems
• Foundation Model
• Vision-based Localization
• Semantic
• Similarity Score
• Image Object
• Need For Training
• Language Model
• Local Approach
• Image Descriptors
• Appearance Variations
• Query Image
• Object Labels
• Viewpoint Changes
• Robot Localization
• Place Recognition
• Camera Viewpoint
• Error Of The Mean
• Local Features
• Natural Language
• Object Detection
• Reference Image
• Translation Error
• Human-robot Interaction
• Reference Trajectory
• Indoor Environments
• Image Retrieval
• Conference Room
• Camera Pose
• Room Type