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相关概念视频

Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Downsampling01:20

Downsampling

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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...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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Upsampling01:22

Upsampling

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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...
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Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge Devices.

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在基于事件的边缘部署 SLAM 中,用于节能运动估计的粗微对比度最大化.

Kyeongpil Min1, Jongin Choi1, Woojoo Lee1

  • 1Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.

Micromachines
|February 27, 2026
PubMed
概括

我们开发了粗微对比最大化 (CCMAX) 技术,以高效地基于事件的运动估计. 在同时定位和映射 (SLAM) 系统中,CCMAX显著降低了计算和能源消耗,实现了与传统方法相比的准确性.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 传感器融合式传感器

背景情况:

  • 基于事件的视觉传感器提供高时间分辨率和低功耗,非常适合边缘机器人和SLAM.
  • 对比度最大化 (CMAX) 是使用事件数据进行自我运动估计的直接几何方法.
  • 传统的CMAX是计算密集的,因为它需要反复处理完整的事件集和高分辨率图像.

研究的目的:

  • 开发一个计算效率高的CMAX变体,用于基于事件的运动估计.
  • 为了降低CMAX的计算和能源成本,而不会牺牲准确性.
  • 在资源有限的边缘平台上实现实时SLAM.

主要方法:

  • 拟议的粗微对比最大化 (CCMAX),是一种计算意识的CMAX方法.
  • 在早期阶段实施了逐步增加IWE分辨率和粗网事件亚抽样.
  • 在最后的优化阶段保留了全分辨率精细化.

主要成果:

  • 在标准基准上,CCMAX的准确性与全分辨率CMAX相美.
  • 浮点交易 (FLOP) 减少了高达42%.
  • 在RISC-V边缘SoC上,已证明能耗降低87%
关键词:
在FPGA的原型设计.从粗到细的优化方法.对比度最大化,对比度最大化边缘计算是一种边缘计算.基于事件的视觉传感器视觉传感器低功率的设计设计.运动估计运动估计视觉同步定位和绘制 (视觉SLAM)

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结论:

  • 在基于事件的运动估计中,CCMAX可显著降低计算成本和能源消耗.
  • 拟议的方法适用于实时边缘SLAM在电力和资源受限制的平台上.
  • CCMAX为先进的机器人应用提供了一个节能前端.