Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling-Reference-Cited by-同舟云学术

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Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling

Published:2020-05 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Akai Naoki,Hirayama Takatsugu,Murase Hiroshi

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9187508/9196508/09196568.pdf?arnumber=9196568

Cited by 14 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

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2. A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems;International Journal of Computer Vision;2024-03-06

3. Particle filter meets hybrid octrees: an octree-based ground vehicle localization approach without learning;Applied Intelligence;2023-09-21

4. Counter-Hypothetical Particle Filters for Single Object Pose Tracking;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

5. Integration of BLE-based proximity detection with particle filter for day-long stability in workplaces;2023 IEEE/ION Position, Location and Navigation Symposium (PLANS);2023-04-24

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