LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping
This work addresses the challenge of building open-set semantic maps for robots, which is incremental as it improves upon existing methods in accuracy and efficiency.
The paper tackles the problem of enabling robots to perform open-set semantic SLAM by tightly coupling object identification, localization, and encoding with probabilistic graphical models, resulting in more accurate object-based SLAM than existing methods while reducing computational overhead.
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. However, utilizing such objects to localize the robot and build an open-set semantic map of the world remains an open research question. In this work, a system of identifying, localizing, and encoding objects is tightly coupled with probabilistic graphical models for performing open-set semantic simultaneous localization and mapping (SLAM). Results are presented demonstrating that the proposed lightweight object encoding can be used to perform more accurate object-based SLAM than existing open-set methods, closed-set methods, and geometric methods while incurring a lower computational overhead than existing open-set mapping methods.