SparseLoc: Sparse Open-Set Landmark-based Global Localization for Autonomous Navigation

Pranjal Paul1    Vineeth Bhat1    Tejas Salian1    Mohammad Omama2    Krishna Murthy Jatavallabhula3    Naveen Arulselvan4    K. Madhava Krishna1   

1 Robotics Research Center, IIIT Hyderabad, India    2 The University of Texas at Austin, USA    3 Meta AI Research    4 Ati Motors, India   




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.