SVN-ICP用新方法估算激光里程计不确定性，无需调参且鲁棒性强。
SVN-ICP: Uncertainty Estimation of ICP-based LiDAR Odometry using Stein Variational Newton
- 基于流形上的斯坦因变分牛顿法，用粒子近似后验分布
- 在多类环境与机器人上均优于顶尖方法，定位更准
- 适合需要可靠置信度的多传感器融合系统，如自动驾驶
本文提出SVN-ICP，一种基于迭代最近点（ICP）的新型算法，可对激光里程计进行不确定性估计，该方法在流形上利用斯坦因变分牛顿（SVN）框架。专为多传感器系统中激光里程计融合设计，即使在激光性能退化的环境中也能实现精确位姿估计和一致的噪声参数推断。通过在斯坦因变分推断框架内使用粒子近似后验分布，SVN-ICP无需显式建模噪声或手动调整参数。为验证有效性，将SVN-ICP集成至简单误差状态卡尔曼滤波器中，并结合惯性测量单元（IMU），在多个涵盖不同环境与机器人类型的基准数据集上进行测试。大量实验结果表明，本方法在复杂场景下超越现有最优方法，同时提供可靠的不确定性估计。
原文摘要 · Abstract (English)
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.
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