# Deep Bayesian ICP Covariance Estimation

arXiv: https://arxiv.org/abs/2202.11607 · HTML: https://mlsanity.com/paper/2202.11607/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [Stochastic Gradient Descent as Approximate Bayesian Inference](https://mlsanity.com/paper/1704.04289/) (2017)
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://mlsanity.com/paper/1806.01411/) (2018)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation](https://mlsanity.com/paper/1908.00598/) (2019)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [IMLS-SLAM: scan-to-model matching based on 3D data](https://mlsanity.com/paper/1802.08633/) (2018)
- [DMLO: Deep Matching LiDAR Odometry](https://mlsanity.com/paper/2004.03796/) (2020)
- [CELLO-3D: Estimating the Covariance of ICP in the Real World](https://mlsanity.com/paper/1810.01470/) (2018)
- [DeepLO: Geometry-Aware Deep LiDAR Odometry](https://mlsanity.com/paper/1902.10562/) (2019)
- [A New Approach to 3D ICP Covariance Estimation](https://mlsanity.com/paper/1909.05722/) (2019)
- [On the Covariance of ICP-based Scan-matching Techniques](https://mlsanity.com/paper/1410.7632/) (2014)
- [Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods](https://mlsanity.com/paper/2007.14943/) (2020)
- [Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping](https://mlsanity.com/paper/1810.01619/) (2018)

## Built on by (most cited first, 2 of 2)

- [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)

Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent developments in deep learning for point clouds, we propose a data-driven approach to learn an error model for ICP. We estimate covariances modeling data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty using a variational Bayesian approach. The system evaluation is performed on LiDAR odometry on different datasets, highlighting good results in comparison to the state of the art.