FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

Raktim Gautam Goswami, Naman Patel, Prashanth Krishnamurthy, Farshad Khorrami; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2011-2020

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds eliminating the need for large maps and descriptors. However their long training times hinder rapid adaptation to new environments. To address this we propose FlashMix which uses a frozen scene-agnostic backbone to extract local point descriptors aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks examining different regularizations and aggregators and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://github.com/raktimgg/FlashMix.