On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization-Reference-Cited by-同舟云学术

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On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization

Published:2022-06-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Intelligent Vehicles Symposium (IV)
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Author:

Joerger Mathieu¹,Wang Julian²,Hassani Ali²

Affiliation:

1. Virginia Polytechnic Institute and State University (Virginia Tech),Kevin T. Crofton Department of Aerospace and Ocean Engineering,Blacksburg,VA,USA,24061

2. Virginia Tech,Kevin T. Crofton Department of Aerospace and Ocean Engineering,Blacksburg,VA,USA,24061

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9826996/9826997/09827445.pdf?arnumber=9827445

Reference22 articles.

1. LiDAR Data Association Risk Reduction, Using Tight Integration with INS

2. Experimental Integrity Evaluation of Tightly-Integrated IMU/LiDAR Including Return-Light Intensity Data

3. Navigation Performance of a LiDAR-Based CNN in a Known Environment

4. L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving

5. Learning to localize using a lidar intensity map;barsan;2nd Conference on Robot Learning (CoRL),2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Uncertainty-aware Sensor Data Anomaly Detection for Autonomous Vehicles;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. Characterizing Perspective Error in Voxel-Based Lidar Scan Matching;NAVIGATION: Journal of the Institute of Navigation;2024

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