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DiffLoc: Diffusion Model for Outdoor LiDAR Localization

Absolute pose regression (APR) estimates global pose in an end-to-end manner achieving impressive results in learn-based LiDAR localization. However compared to the top-performing methods reliant on 3D-3D correspondence matching APR's accuracy still has room for improvement. We recognize APR's lack of robust features learning and iterative denoising process leads to suboptimal results. In this paper we propose DiffLoc a novel framework that formulates LiDAR localization as a conditional generation of poses. First we propose to utilize the foundation model and static-object-aware pool to learn robust features. Second we incorporate the iterative denoising process into APR via a diffusion model conditioned on the learned geometrically robust features. In addition due to the unique nature of diffusion models we propose to adapt our models to two additional applications: (1) using multiple inferences to evaluate pose uncertainty and (2) seamlessly introducing geometric constraints on denoising steps to improve prediction accuracy. Extensive experiments conducted on the Oxford Radar RobotCar and NCLT datasets demonstrate that DiffLoc outperforms better than the stateof-the-art methods. Especially on the NCLT dataset we achieve 35% and 34.7% improvement on position and orientation accuracy respectively. Our code is released at https://github.com/liw95/DiffLoc.

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