Global localization is a critical capability for autonomous navigation, yet existing dense-LiDAR approaches are storage-heavy and scale poorly.
**SparseLoc** introduces a compact and generalizable localization framework by leveraging open-vocabulary vision-language models to build semantic-topometric landmark maps.
Unlike traditional methods, SparseLoc creates sparse landmark representations with semantic associations that can be robustly matched during inference.
A Monte Carlo localization pipeline uses these sparse maps and camera observations, and is enhanced by a late-stage gradient-based optimization module for fine-grained correction.
Using just 1⁄500 of the points of dense maps, the system achieves sub-5 meter position and 2° heading error on KITTI.
It also demonstrates strong cross-dataset generalization and robust recovery from kidnapped robot scenarios.
SparseLoc pushes the boundary of memory-efficient, semantically aware, vision-language-driven localization.
