Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Upsampling01:22

Upsampling

581
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
581
Downsampling01:20

Downsampling

605
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
605
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.2K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
12.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

FAVAR-GAT: A Hybrid Temporal-structural Model for Drug-target Binding Affinity Prediction.

Current drug targets·2026
Same author

Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality.

Scientific reports·2026
Same author

Adaptive emergency response and dynamic crowd navigation for mobile robot using deep reinforcement learning.

Frontiers in robotics and AI·2025
Same author

A multi-robot collaborative manipulation framework for dynamic and obstacle-dense environments: integration of deep learning for real-time task execution.

Frontiers in robotics and AI·2025
Same author

Endothelial ActRIIA inhibition protects the cardiac microvasculature in severe viral respiratory infection.

Research square·2025
Same author

CARE: towards customized assistive robot-based education.

Frontiers in robotics and AI·2025

相关实验视频

Updated: Jan 15, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
16:52

Test Samples for Optimizing STORM Super-Resolution Microscopy

Published on: September 6, 2013

31.6K

VIO-GO:优化基于事件的SLAM参数,在高动态范围场景中提供强大的性能.

Saber Sakhrieh1, Abhilasha Singh1, Jinane Mounsef1

  • 1Electrical Engineering and Computing Sciences Department, Rochester Institute of Technology, Dubai, United Arab Emirates.

Frontiers in robotics and AI
|October 6, 2025
PubMed
概括

这项研究通过使用事件摄像头和一种基于VIO梯度的优化 (VIO-GO) 方法来增强工业4.0机器人中的视觉惯性计数 (VIO). 在具有挑战性的工业环境中,VIO-GO显著提高了定位精度.

关键词:
批量梯度下降的下降方式动态和低光环境的环境.边缘图像 图像边缘图像活动 SLAM SLAM 活动优化的优化优化优化.视觉惯性公里计.

更多相关视频

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
14:23

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Published on: March 6, 2018

11.4K
Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy
09:19

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Published on: August 29, 2025

565

相关实验视频

Last Updated: Jan 15, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
16:52

Test Samples for Optimizing STORM Super-Resolution Microscopy

Published on: September 6, 2013

31.6K
Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
14:23

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Published on: March 6, 2018

11.4K
Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy
09:19

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Published on: August 29, 2025

565

科学领域:

  • 机器人和自动化机器人与自动化
  • 计算机视觉 计算机视觉
  • 传感器融合式传感器

背景情况:

  • 工业4.0机器人在动态,低光环境中面临着挑战.
  • 现有的视觉惯性测距 (VIO) 系统在这些条件下难以保证可靠性.
  • 事件摄像机在具有挑战性的工业环境中提供了改进传感的潜力.

研究的目的:

  • 增强VIO系统,在动态和低光工业4.0环境中提供强大的性能.
  • 为事件同时定位和映射 (SLAM) 参数引入一种新的优化方法.
  • 在复杂的工业场景中实现精确可靠的机器人定位和映射.

主要方法:

  • 生物启发事件摄像机与传统视频和惯性数据集成,用于状态估计.
  • 开发使用批量梯度下降 (BGD) 的基于VIO梯度优化 (VIO-GO) 方法.
  • 为事件SLAM使用运动补偿图像来表示事件数据的自动参数调整.

主要成果:

  • 与固定参数方法相比,平均位置误差 (MPE) 得到了60%的改进.
  • 对于精确的VIO性能,VIO-GO始终确定了最佳参数.
  • 观察到MPE减少了24%,而参数复杂性增加 (VIO-GO8与VIO-GO2).

结论:

  • 拟议的VIO-GO方法有效地优化事件SLAM参数,用于实际应用.
  • 增强的VIO系统在具有挑战性的工业环境中展示了强大而精确的本地化能力.
  • 这种方法具有可扩展性,适用于工业4.0.0中的自适应机器人系统.