# CELLO-3D: Estimating the Covariance of ICP in the Real World

arXiv: https://arxiv.org/abs/1810.01470 · HTML: https://mlsanity.com/paper/1810.01470/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [Learning-based Localizability Estimation for Robust LiDAR Localization](https://mlsanity.com/paper/2203.05698/) (2022)
- [Stein ICP for Uncertainty Estimation in Point Cloud Matching](https://mlsanity.com/paper/2106.03287/) (2021)
- [Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation](https://mlsanity.com/paper/2406.02125/) (2024)
- [DOA: A Degeneracy Optimization Agent with Adaptive Pose Compensation Capability based on Deep Reinforcement Learning](https://mlsanity.com/paper/2507.19742/) (2025)
- [Deep Bayesian ICP Covariance Estimation](https://mlsanity.com/paper/2202.11607/) (2022)
- [Learning minimal volume uncertainty ellipsoids](https://mlsanity.com/paper/2405.02441/) (2024)
- [Global Consistent Point Cloud Registration Based on Lie-algebraic Cohomology](https://mlsanity.com/paper/2208.07103/) (2022)
- [Neural Error Covariance Estimation for Precise LiDAR Localization](https://mlsanity.com/paper/2501.02558/) (2025)
- [An End-to-End Learning-Based Multi-Sensor Fusion for Autonomous Vehicle Localization](https://mlsanity.com/paper/2503.05088/) (2025)
- [CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments](https://mlsanity.com/paper/2505.06483/) (2025)
- [Human-Interpretable Uncertainty Explanations for Point Cloud Registration](https://mlsanity.com/paper/2509.18786/) (2025)
- [Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation](https://mlsanity.com/paper/2509.18954/) (2025)

## Abstract (arXiv)

The fusion of Iterative Closest Point (ICP) reg- istrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. In this paper, we study the estimation of this uncertainty in the form of a covariance. First, we scrutinize the limitations of existing closed-form covariance estimation algorithms over 3D datasets. Then, we set out to estimate the covariance of ICP registrations through a data-driven approach, with over 5 100 000 registrations on 1020 pairs from real 3D point clouds. We assess our solution upon a wide spectrum of environments, ranging from structured to unstructured and indoor to outdoor. The capacity of our algorithm to predict covariances is accurately assessed, as well as the usefulness of these estimations for uncertainty estimation over trajectories. The proposed method estimates covariances better than existing closed-form solutions, and makes predictions that are consistent with observed trajectories.