LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and
  Mapping
Kurran Singh Tim Magoun John J. Leonard
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.