Misalignment Recognition Using Markov Random Fields With Fully Connected Latent Variables for Detecting Localization Failures-Reference-Cited by-同舟云学术

Misalignment Recognition Using Markov Random Fields With Fully Connected Latent Variables for Detecting Localization Failures

Published:2019-10 Issue:4 Volume:4 Page:3955-3962
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Akai Naoki^ORCID,Morales Luis Yoichi^ORCID,Hirayama Takatsugu^ORCID,Murase Hiroshi

Funder

JST COI

KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/8764082/08767945.pdf?arnumber=8767945

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

1. A framework for collaborative multi-robot mapping using spectral graph wavelets;The International Journal of Robotics Research;2024-04-15

2. Localization Fault Detection Method using 2D LiDAR and Fisheye Camera for an Autonomous Mobile Robot Control;2024 SICE International Symposium on Control Systems (SICE ISCS);2024-03-18

3. Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges;IEEE Transactions on Intelligent Transportation Systems;2024-02

4. Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking;IEEE Transactions on Robotics;2023-08

5. Reliable Monte Carlo localization for mobile robots;Journal of Field Robotics;2023-01-05