CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation-Reference-Cited by-同舟云学术

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

Published:2022-12-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)
language:
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Author:

Sun Leyuan¹,Ding Guanqun²,Yoshiyasu Yusuke³,Kanehiro Fumio⁴

Affiliation:

1. University of Tsukuba,Graduate School of Science and Technology,Department of Intelligent and Mechanical Interaction Systems,Tsukuba,Ibaraki,Japan

2. National Institute of Advanced Industrial Science and Technology (AIST),Digital Architecture Research Center (DARC),Tokyo,Japan

3. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST),Computer Vision Research Team,Japan

4. CNRS-AIST Joint Robotics Laboratory (JRL), IRL, National Institute of Advanced Industrial Science and Technology (AIST).

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10011626/10011636/10011808.pdf?arnumber=10011808

Reference41 articles.

1. Geometric Loss Functions for Camera Pose Regression with Deep Learning

2. Multi-task learning using uncer-tainty to weigh losses for scene geometry and semantics;kendall;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018

Cited by 1 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. TransFusionOdom: Transformer-Based LiDAR-Inertial Fusion Odometry Estimation;IEEE Sensors Journal;2023-09-15

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