Deep Bayesian ICP Covariance Estimation
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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.
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
20 of 35 references.
References · 20
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
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- 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
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- 2017Stochastic Gradient Descent as Approximate Bayesian Inferencecited by 300
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- 2017DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networkscited by 175
- 2018A micro Lie theory for state estimation in roboticscited by 124
- 2019Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagationcited by 74
- 2019LO-Net: Deep Real-time Lidar Odometrycited by 60
8 more
- 2018IMLS-SLAM: scan-to-model matching based on 3D datacited by 40
- 2020DMLO: Deep Matching LiDAR Odometrycited by 20
- 2018CELLO-3D: Estimating the Covariance of ICP in the Real Worldcited by 13
- 2019DeepLO: Geometry-Aware Deep LiDAR Odometrycited by 9
- 2019A New Approach to 3D ICP Covariance Estimationcited by 9
- 2014On the Covariance of ICP-based Scan-matching Techniquescited by 5
- 2020Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methodscited by 4
- 2018Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mappingcited by 2
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Citing papers per year: 2025: 2.
2 papers.