.png){kind=icon}
返回
Quantized Self-Supervised Local Feature for Real-Time Robot Indirect VSLAM
.png){kind=icon}
PRE
AI
DOI：10.1109/TMECH.2021.3085326.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The indirect visual simultaneous localization and mapping (VSLAM) is widely used in robot localization and navigation, thanks to its potential to achieve high localization accuracy with the local feature observations. However, the existing local features are subject to drift and mismatches under various visual conditions, which causes a degrading in localization accuracy and tracking loss. This article proposes a quantized self-supervised local feature for the indirect VSLAM to handle the environmental interference in robot localization tasks. A joint feature detection and description network is built in a lightweight manner to extract local features in real time. The network is iteratively trained by a self-supervised learning strategy, and the extracted local features are quantized by an orthogonal transformation for efficiency. We utilize frame-wise matching in Hamming space and bundle adjustment to establish a parallel indirect VSLAM. The proposed VSLAM demonstrates outstanding localization accuracy and tracking stability in the evaluation on multiple datasets and robustness in real-world experiments with the Realsense D435 RGB-D sensor. The efficiency experiment on Jetson TX2 indicates that the quantized self-supervised local feature is suitable for feature-based tasks on edge computing platforms.
Keyword:
Feature extraction
Feature detection
Robustness
Visualization
Real-time systems
Tensors
Task analysis
Descriptor quantization
indirect VSLAM
local feature
robustness
self-supervised learning
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
I
IF:
7.3
论文数:
5.4K
被引数:
2.4W
机构
引用论文
AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning PipelineAutoMorph: 通过深度学习管道自动量化视网膜血管形态
OAAI
OAAI分享收藏
1H-MR spectroscopy differentiates mild cognitive impairment from normal brain aging.png)
Neuroreport
NeuroreportIF0
PREAI
分享收藏Reconsidering the effects of urban form on PM2.5 concentrations: an urban shrinkage perspective
PREAI
分享收藏
查看更多内容