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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
526
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

600
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
465
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

448
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...
448
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

58
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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相关实验视频

Updated: Jun 9, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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不确定性意识深度网络用于移动机器人的视觉惯性计数.

Jimin Song1, HyungGi Jo1, Yongsik Jin2

  • 1Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括

本研究引入了一个不确定性意识深度网络 (UD-Net),以增强自主系统的视觉惯性测距 (VIO). UD-Net 改进了深度估计和过,在复杂的驾驶场景中显著提高了 VIO 的性能.

关键词:
深度估计估计的估计.停车场数据集 停车场数据集同时定位和绘制地图.不确定性估计估计的不确定性视觉惯性测距仪使用视觉惯性测距仪

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相关实验视频

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科学领域:

  • 机器人技术和自主系统
  • 计算机视觉 计算机视觉
  • 传感器融合式传感器

背景情况:

  • 同时定位和映射 (SLAM) 对自动驾驶汽车和机器人至关重要.
  • 惯性测量单元 (IMU) 提供了成本效益高的运动估计,但受到噪声的影响.
  • 视觉惯性计数 (VIO) 结合了摄像机和IMU,以获得强大的空间理解.

研究的目的:

  • 引入一个不确定性意识深度网络 (UD-Net),以改善深度和不确定性地图估计.
  • 开发一个新的损失函数用于训练UD-Net.
  • 通过使用不确定性地图,通过过不可靠的深度值来提高VIO性能.

主要方法:

  • 开发了UD-Net,用于同时进行深度和不确定性地图估计.
  • 为UD-Net培训量身定制了一种新的损失功能.
  • 实施了使用不确定性图表的过机制,以改进VIO的深度数据.

主要成果:

  • UD-Net成功估计了深度和不确定性地图.
  • 拟议的VIO算法与现有方法相比,显示出更高的性能.
  • 在KITTI和定制数据集上的实验验证了该方法的有效性.

结论:

  • 不确定性意识深度网络显著提高了VIO的准确性.
  • 过基于不确定性的不可靠的深度数据是提高自主系统感知度的关键.
  • 拟议的方法为现实世界的自动驾驶应用提供了强大的解决方案.