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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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.
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Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
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Relative Motion Analysis - Acceleration

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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Related Experiment Video

Updated: May 28, 2026

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
07:24

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

ES-ATRK: A Global Bundle Adjustment Initialisation Method for Event-Based Stereo Visual Inertial SLAM System Using

Junyang Zhao1, Han Yu1, Zhili Zhang1

  • 1School of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces ES-ATRK, an adaptive method improving event-based SLAM initialization. It enhances robustness and depth accuracy, significantly boosting localization performance in challenging environments.

Keywords:
ES-ATRK algorithmabsolute trajectory erroradaptive thresholdevent-based stereo visual inertial SLAM systemsglobal bundle adjustmentreprojection residualsrobust kernels

Related Experiment Videos

Last Updated: May 28, 2026

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
07:24

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Event-based stereo visual inertial SLAM systems face challenges in initialization, including poor robustness, depth recovery errors, and limited scene adaptability.
  • Existing methods struggle with outlier interference and dynamic thresholding, hindering performance in complex environments.

Purpose of the Study:

  • To propose a novel global Bundle Adjustment (BA) initialization method, ES-ATRK, for event-based stereo visual inertial SLAM systems.
  • To enhance robustness, depth recovery accuracy, and scene adaptability during the SLAM initialization phase.

Main Methods:

  • Spatio-temporal fusion of event and visual features for 3D map generation via feature triangulation.
  • Integration of a robust kernel function with adaptive thresholding into the global BA optimization pipeline.
  • Scene-adaptive thresholding using quantile distribution of residuals, validity checks, and iterative smoothing.

Main Results:

  • ES-ATRK achieved over 10% localization accuracy improvement on 85% of VECtor dataset scenes compared to ESVIO and USLAM.
  • Demonstrated approximately double the ATE performance in high-dynamic scenes compared to ESIO.
  • Showcased superior feature tracking and trajectory estimation on DSEC, HKU, and MVSEC datasets, including large-scale outdoor scenarios.

Conclusions:

  • ES-ATRK significantly enhances the robustness and depth recovery performance of event-based stereo visual inertial SLAM initialization.
  • The method reduces overall positioning error and improves adaptability in challenging environments like low-texture, high-dynamic, and HDR conditions.