Unleashing the Power of Data Generation in One-Pass Outdoor LiDAR Localization (ACM MM2025)

Yidong Chen, Qi Li, Yuyang Yang, Wen Li, Sheng Ao, Cheng Wang

Abstract:

Point cloud regression localization technology has a wide range of
applications in the multimedia field. For example, in virtual reality
and augmented reality, accurate point cloud localization can significantly enhance the user experience. Recently, point cloud pose
regression algorithms based on APR (Absolute Pose Regression) and
SCR (Scene Coordinate Regression) have achieved near sub-meter
accuracy, requiring multiple repetitive trajectories for training. The
key to their success lies in the diversity of viewpoints, temporal
changes, and trajectories,which is resource-consuming. However, due to the errors in GPS/INS, the coupling between trajectories
is not ideal, and the stability of re-localization is insufficient. Since
LiDAR has covered most of the scene, single-shot localization has
the potential to approach or even surpass multi-trajectory localization methods through pose enhancement. Specifically, we present
Pose Enhancement Localization (PELoc), which feeds one trajectory,
proposing SSDA (Single-shot Data Augmentation) and LTI (LiDAR
Trajectories-coupled Interpolation) to simulate different driving
poses, and we introduce KP-CL (Key Points Contrastive Learning) through feature perturbation to mitigate the differences in
viewpoint/temporal phase transformations in similar scenes across different trajectories. Our algorithm has been tested on the Oxford,
QE-Oxford, and NCLT datasets, where single-shot localization accuracy can approach near sub-meter level on QE-Oxford and NCLT.
The code will be published in https://github.com/Eaton2022/PELoc.