Deep Bayesian ICP Covariance Estimation
arXiv:2202.11607 · doi:10.1109/ICRA46639.2022.9811899
Abstract
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.
References in corpus (6)
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
- Stochastic Gradient Descent as Approximate Bayesian Inference
- A New Approach to 3D ICP Covariance Estimation
- Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping