.png){kind=icon}
返回
Unsupervised Monocular Visual Odometry Based on Confidence Evaluation
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2021.3053412.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the rapid development of autonomous vehicle technologies, how to perform high-precision localization in unknown complex outdoor environment has become an important issue. Visual odometry is one of the low-cost and the most widely utilized localization methods. Traditional methods predict relative pose based on the principle of multi-view geometry, which is sensitive to camera parameters and environmental changes. This paper studies deep learning-based methods which can be more robust. A novel end-to-end unsupervised visual odometry framework based on confidence evaluation is proposed. Its process can be divided into two stages. The first is predicting the initial relative pose transformation with the help of confidence mask which is generated by measuring the relative similarity of geometric corresponding regions in associated images. The second is evaluating the confidence of the output pose estimate based on the trajectory geometric consistency and then refining it. Quantitative and qualitative evaluation of the proposed approach on KITTI dataset are presented to demonstrate its effectiveness in improving pose estimation accuracy and robustness.
Keyword:
Visual odometry
Pose estimation
Feature extraction
Cameras
Visualization
Trajectory
Optimization
Computer vision
pose estimation
unsupervised learning
visual odometry
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
Perovskite solid electrolytes: Structure, transport properties and fuel cell applications钙钛矿固体电解质: 结构、输运性质和燃料电池应用
PREAI
分享收藏Evaluation of qualitative and quantitative data of Y-90 imaging in SPECT/CT and PET/CT phantom studies.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏分享收藏
查看更多内容