Deep Bayesian ICP Covariance Estimation-Reference-Cited by-同舟云学术

Deep Bayesian ICP Covariance Estimation

Published:2022-05-23 Issue: Volume: Page:
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Container-title:2022 International Conference on Robotics and Automation (ICRA)
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Author:

De Maio Andrea¹,Lacroix Simon¹

Affiliation:

1. LAAS-CNRS, Universite de Toulouse,Toulouse,France,31031

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9811522/9811357/09811899.pdf?arnumber=9811899

Reference35 articles.

1. Tracking a depth camera: Parameter exploration for fast ICP

2. FlowNet3D: Learning Scene Flow in 3D Point Clouds

3. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning;gal;Proc of the International Conference on Machine Learning (ICML),0

4. Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation

5. Challenging data sets for point cloud registration algorithms

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1. Correcting Motion Distortion for LIDAR Scan-to-Map Registration;IEEE Robotics and Automation Letters;2024-02

2. X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments;IEEE Transactions on Robotics;2024

3. Principled ICP Covariance Modelling in Perceptually Degraded Environments for the EELS Mission Concept;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

4. Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29