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関連する概念動画

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

448
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...
448
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

168
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
168
Observational Learning01:12

Observational Learning

310
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
310
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

530
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...
530
Inertial Frames of Reference01:03

Inertial Frames of Reference

7.4K
Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
7.4K
Perceptual Constancy01:12

Perceptual Constancy

528
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
528

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関連する実験動画

Updated: Sep 9, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

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照明が変化する環境で学習ベースの線特性を備えた堅固な視覚慣性オドメトリー

Xinkai Li1, Cong Liu1, Xu Yan1

  • 1James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ

DeepLine-VIOは,学習された,照明不変の線特性を使用して視覚慣性オドメトリー (VIO) を強化します. この頑丈なフレームワークは 挑戦的な低テクスチャ環境での軌道の精度を向上させます

科学分野:

  • ロボット
  • コンピュータ・ビジョン
  • 人工知能

背景:

  • Visual-Inertial Odometry (VIO) システムは,テクスチャが少ない環境で苦戦しています.
  • ラインの特徴を使用する既存の方法は,異なる照明下で劣化します.

研究 の 目的:

  • 困難な視覚条件下での性能低下を克服する堅牢な VIO フレームワーク,DeepLine-VIO を開発する.
  • VIOの視覚的な特徴の幾何学的な一貫性と照明不変性を改善する.

主な方法:

  • ポイント特征と慣性データで,アトラクションフィールドベースの深層ネットワークで抽出した学習された線特征の統合.
  • マルチモダルの観測の緊密なカップリングのためのスライディングウィンドウ最適化フレームワークを使用します.
  • 信頼性の高いラインセグメント抽出のための幾何学的なフィルタリングとパラメータ化戦略の実装.

主要な成果:

  • DeepLine-VIOは,EuRoCのデータセットで既存の点と線ベースのVIO方法よりも優れたパフォーマンスを示しています.
  • 照明の乱射下では,絶対軌道誤差 (ATE) が最大15.87%減少し,相対姿勢誤差 (RPE) が最大58.45%減少した.
  • 視覚的に劣化し,照明が変化する条件で一貫した優れた性能.
キーワード:
ディープラーニングラインの特徴同時ローカライゼーションとマッピング (SLAM)ビジュアル・イナーシアル・オドメトリー (VIO)

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Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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関連する実験動画

Last Updated: Sep 9, 2025

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Published on: October 27, 2016

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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結論:

  • DeepLine-VIOは, VIOシステムにとって,困難な環境で,より高い強度と精度を提供します.
  • 学習した,照明不変の線形は,VIOの性能を改善するために不可欠です.
  • 提案されたフレームワークは,視覚的に劣化したシナリオで視覚的なオドメトリーのための信頼性の高いソリューションを提供します.