# SANDRO: a Robust Solver with a Splitting Strategy for Point Cloud Registration

arXiv: https://arxiv.org/abs/2503.07743 · HTML: https://mlsanity.com/paper/2503.07743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration](https://mlsanity.com/paper/1905.04153/) (2019)
- [Guaranteed Outlier Removal for Point Cloud Registration with Correspondences](https://mlsanity.com/paper/1711.10209/) (2017)
- [End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds](https://mlsanity.com/paper/2003.05855/) (2020)
- [3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm](https://mlsanity.com/paper/2310.10023/) (2023)
- [Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems](https://mlsanity.com/paper/2206.09215/) (2022)
- [Non-iterative rigid 2D/3D point-set registration using semidefinite programming](https://mlsanity.com/paper/1501.00630/) (2015)
- [An Adaptive Graduated Nonconvexity Loss Function for Robust Nonlinear Least Squares Solutions](https://mlsanity.com/paper/2305.06869/) (2023)
- [Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions](https://mlsanity.com/paper/2410.05256/) (2024)

## Abstract (arXiv)

Point cloud registration is a critical problem in computer vision and robotics, especially in the field of navigation. Current methods often fail when faced with high outlier rates or take a long time to converge to a suitable solution. In this work, we introduce a novel algorithm for point cloud registration called SANDRO (Splitting strategy for point cloud Alignment using Non-convex anD Robust Optimization), which combines an Iteratively Reweighted Least Squares (IRLS) framework with a robust loss function with graduated non-convexity. This approach is further enhanced by a splitting strategy designed to handle high outlier rates and skewed distributions of outliers. SANDRO is capable of addressing important limitations of existing methods, as in challenging scenarios where the presence of high outlier rates and point cloud symmetries significantly hinder convergence. SANDRO achieves superior performance in terms of success rate when compared to the state-of-the-art methods, demonstrating a 20% improvement from the current state of the art when tested on the Redwood real dataset and 60% improvement when tested on synthetic data.