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Relative Motion Analysis using Rotating Axes01:25

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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.
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Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
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Wheel-Mounted Inertial Datasets.

Dusan Nemec1, Gal Versano2, Vojtech Simak1

  • 1Faculty of Electrical Engineering and Information Technology, University of Zilina, Zilina, Slovakia.

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|December 12, 2025
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This study introduces the Wheel-Mounted Inertial (WMI) dataset, demonstrating that wheel-mounted inertial sensors reduce drift better than chassis-mounted ones. This new dataset aids research in advanced sensor applications.

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion and Navigation

Background:

  • Inertial sensors are crucial for vehicle navigation, but chassis mounting leads to significant drift.
  • Wheel-mounted inertial sensors offer improved drift mitigation compared to chassis-mounted alternatives.
  • A lack of publicly available datasets hinders research and development in wheel-mounted inertial sensing.

Purpose of the Study:

  • To introduce the Wheel-Mounted Inertial (WMI) dataset, addressing the gap in publicly available data for wheel-mounted inertial sensors.
  • To provide a comprehensive resource for developing and validating algorithms utilizing wheel-mounted inertial sensor data.

Main Methods:

  • Recorded data using two distinct platforms: an omni-directional robot with 5 IMUs and a passenger car with 9 IMUs.
  • Equipped each platform with inertial measurement units (IMUs) mounted on every wheel.
  • Collected 64.04 minutes of recordings per IMU, totaling 490 minutes across all sensors, with associated ground truth trajectories.

Main Results:

  • The WMI dataset comprises synchronized data from multiple IMUs positioned on vehicle wheels.
  • The dataset includes ground truth trajectories essential for sensor fusion and navigation algorithm development.
  • This resource facilitates the evaluation of both model-based and data-driven approaches for wheel-mounted inertial sensors.

Conclusions:

  • The WMI dataset is a valuable contribution to the field of inertial sensing for vehicle navigation.
  • It enables researchers to develop and test advanced algorithms for improved localization and state estimation.
  • The availability of this dataset will accelerate innovation in autonomous systems and robotics.