.png){kind=icon}
返回
Deep Learning Techniques for Visual SLAM: A Survey
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3249661.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable attention in recent years. This task involves using visual sensors to localize a robot while simultaneously constructing an internal representation of its environment. Traditional VSLAM methods involve the laborious hand-crafted design of visual features and complex geometric models. As a result, they are generally limited to simple environments with easily identifiable textures. Recent years, however, have witnessed the development of deep learning techniques for VSLAM. This is primarily due to their capability of modeling complex features of the environment in a completely data-driven manner. In this paper, we present a survey of relevant deep learning-based VSLAM methods and suggest a new taxonomy for the subject. We also discuss some of the current challenges and possible directions for this field of study.
Keyword:
Deep learning
Simultaneous localization and mapping
Estimation
Visualization
Visual odometry
Feature extraction
Learning systems
Location awareness
Robots
Taxonomies
Robot sensing systems
Visual SLAM
deep learning
joint learning
active learning
survey
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Judgment Capacity, Fear of Falling, and the Risk of Falls in Community-Dwelling Older Adults: The Progetto Veneto Anziani Longitudinal Study社区居住的老年人的判断能力，对跌倒的恐惧和跌倒的风险: Progetto Veneto Anziani纵向研究
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏分享收藏分享收藏
查看更多内容