Trustworthy and Efficient Map-free LiDAR Localization via Scan-Pose Alignment and Flow Matching

Reliable map-free LiDAR relocalization requires not only accurate and efficient pose estimation but also trustworthy confidence estimation for detecting localization failures. However, estimating reliability to ensure trustworthy LiDAR relocalization remains largely unexplored. Also, existing LiDAR pose regression methods suffer from accuracy and efficiency trade-off. To address this limitation, we propose \textbf{SPAR}, a model-agnostic confidence estimator that reformulates localization reliability as scan-pose alignment. Given a query scan and a predicted pose, SPAR measures their compatibility in a learned embedding space and explicitly optimizes confidence scores to correlate scan with pose. In addition, we introduce \textbf{VeLoc}, an efficient and accurate pose regressor based on flow-matching for efficient pose refinement. Experiments on the Oxford and NCLT datasets demonstrate that the proposed framework achieves a favorable balance between efficiency and accuracy for LiDAR relocalization while enabling reliable localization failure detection across different localization backbones.