# Analysis of Robust Functions for Registration Algorithms

arXiv: https://arxiv.org/abs/1810.01474 · HTML: https://mlsanity.com/paper/1810.01474/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [LiDAR-Event Stereo Fusion with Hallucinations](https://mlsanity.com/paper/2408.04633/) (2024)
- [Metadata-free Georegistration of Ground and Airborne Imagery](https://mlsanity.com/paper/2503.04927/) (2025)
- [Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities](https://mlsanity.com/paper/2210.09678/) (2022)
- [Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights](https://mlsanity.com/paper/2309.08731/) (2023)
- [BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration](https://mlsanity.com/paper/2407.08729/) (2024)
- [Symmetrized Robust Procrustes: Constant-Factor Approximation and Exact Recovery](https://mlsanity.com/paper/2207.08592/) (2022)
- [Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm](https://mlsanity.com/paper/2403.05666/) (2024)
- [Scalable and Differentiable Point-Cloud Registration Using Maximum Mean Discrepancy](https://mlsanity.com/paper/2606.27818/) (2026)

## Abstract (arXiv)

Registration accuracy is influenced by the presence of outliers and numerous robust solutions have been developed over the years to mitigate their effect. However, without a large scale comparison of solutions to filter outliers, it is becoming tedious to select an appropriate algorithm for a given application. This paper presents a comprehensive analyses of the effects of outlier filters on the ICP algorithm aimed at mobile robotic application. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. Furthermore, the influence of tuning parameters have been thoroughly explored. The experimental results show that most outlier filters have similar performance if they are correctly tuned. Nonetheless, filters such as Var. Trim., Cauchy, and Cauchy MAD are more stable against different environment types. Interestingly, the simple norm L1 produces comparable accuracy, while been parameterless.