Detection of Localization Failures Using Markov Random Fields With Fully Connected Latent Variables for Safe LiDAR-Based Automated Driving-Reference-Cited by-同舟云学术

Detection of Localization Failures Using Markov Random Fields With Fully Connected Latent Variables for Safe LiDAR-Based Automated Driving

Published:2022-10 Issue:10 Volume:23 Page:17130-17142
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Akai Naoki¹^ORCID,Akagi Yasuhiro²^ORCID,Hirayama Takatsugu³^ORCID,Morikawa Takayuki⁴,Murase Hiroshi⁵^ORCID

Affiliation:

1. Graduate School of Engineering, Nagoya University, Nagoya, Japan

2. Institute of Innovation for Future Society (MIRAI), Nagoya University, Nagoya, Japan

3. Faculty of Human Environment, University of Human Environments, Okazaki, Japan

4. Global Research Institute for Mobility in Society, Nagoya University, Nagoya, Japan

5. Graduate School of Informatics, Nagoya University, Nagoya, Japan

Funder

Center of Innovation COI Program through the Japan Science and Technology Agency Nagoya-COI Mobility Society leading to an Active and Joyful Life for Elderly

JSPS KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9916643/09756887.pdf?arnumber=9756887

Reference61 articles.

1. Accurate Mix-Norm-Based Scan Matching

2. Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter

3. CMRNet: Camera to LiDAR-Map Registration

4. VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry

5. Deep Auxiliary Learning for Visual Localization and Odometry

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