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

Abstract

Global localization is a critical problem in autonomous navigation, enabling precise positioning without reliance on GPS. Modern techniques often depend on dense LiDAR maps, which, while precise, require extensive storage and computational resources. Alternative approaches have explored sparse maps and learned features, but suffer from poor robustness and generalization. We propose SparseLoc<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, a global localization framework that leverages vision-language foundation models to generate sparse, semantic-topometric maps in a zero-shot manner. Our approach combines this representation with Monte Carlo localization enhanced by a novel late optimization strategy for improved pose estimation. By constructing compact yet discriminative maps and refining poses through retrospective optimization, SparseLoc overcomes limitations of existing sparse methods, offering a more efficient and robust solution. Our system achieves over 5× improvement in localization accuracy compared to existing sparse mapping techniques. Despite utilizing only 1/500<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> of the points used by dense methods, it achieves comparable performance, maintaining average global localization error below 5m and 2° on KITTI. We further demonstrate the practical applicability of our method through cross-sequence localization experiments and downstream navigation tasks.