This letter introduces SVN-ICP, a novel Iterative Closest Point (ICP) algorithm with uncertainty estimation that leverages Stein Variational Newton (SVN) on manifold. Designed specifically for fusing LiDAR odometry in multisensor systems, the proposed method ensures accurate pose estimation and consistent noise parameter inference, even in LiDAR-degraded environments. By approximating the posterior distribution using particles within the Stein Variational Inference framework, SVN-ICP eliminates the need for explicit noise modeling or manual parameter tuning. To evaluate its effectiveness, we integrate SVN-ICP into a simple error-state Kalman filter alongside an IMU and test it across multiple datasets spanning diverse environments and robot types. Extensive experimental results demonstrate that our approach outperforms best-in-class methods on challenging scenarios while providing reliable uncertainty estimates.

翻译：本文提出SVN-ICP，一种基于流形上Stein变分牛顿（SVN）进行不确定性估计的新型迭代最近点（ICP）算法。该方法专为多传感器系统中的激光雷达里程计融合而设计，即使在激光雷达性能退化的环境中，也能确保精确的位姿估计和一致的噪声参数推断。通过在Stein变分推断框架中使用粒子近似后验分布，SVN-ICP无需显式噪声建模或手动参数调优。为评估其有效性，我们将SVN-ICP与IMU共同集成至一个简单的误差状态卡尔曼滤波器中，并在涵盖多样化环境与机器人类型的多个数据集上进行测试。大量实验结果表明，我们的方法在具有挑战性的场景中优于现有最佳方法，并能提供可靠的不确定性估计。