SVN-ICP: Uncertainty Estimation of ICP-Based LiDAR Odometry Using Stein Variational Newton

This letter introduces <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SVN-ICP</monospace>, 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, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SVN-ICP</monospace> eliminates the need for explicit noise modeling or manual parameter tuning. To evaluate its effectiveness, we integrate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SVN-ICP</monospace> 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. We release our code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LIS-TU-Berlin/SVN-ICP.git</uri>. A high-resolution video demonstration is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/c4QsMd1weik</uri>.