Tejas Salian

Selected Publications

1. arXiv

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

   Pranjal Paul*, Vineeth Bhatt*, Tejas Salian, and 4 more authors

   arXiv preprint arXiv:2503.23465 2025

   Abs Bib HTML Website Video

   SparseLoc is a global localization framework for city-scale scenarios that leverages vision-language foundation models to generate sparse semantic topometric maps in a zero-shot manner. It combines this map representation with a Monte Carlo localization scheme enhanced by a novel late optimization strategy, ensuring improved pose estimation. By constructing compact yet highly discriminative maps and refining localization through a carefully designed optimization schedule, SparseLoc overcomes the limitations of existing topometric localization methods, offering a better solution for global localization at the scale of a city. Our system achieves over a 5× improvement in localization accuracy compared to existing sparse mapping techniques. Despite utilizing only 1/500th of the points of dense mapping methods, it achieves comparable performance, maintaining an average global localization error below 5m and 2° on KITTI Sequences.

   @article{paul2025sparseloc,
     title = {SparseLoc: Sparse Open-Set Landmark-based Global Localization for Autonomous Navigation},
     author = {Paul*, Pranjal and Bhatt*, Vineeth and Salian, Tejas and Omama, Mohammad and Jatavallabhula, K.M. and Arulselvan, Naveen and Krishna, Madhava},
     journal = {arXiv preprint arXiv:2503.23465},
     year = {2025},
     video = {https://www.youtube.com/watch?v=PmMC49SNj2k}
   }