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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.
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VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
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Event-based feature tracking in a visual inertial odometry framework.

José Ribeiro-Gomes1, José Gaspar1, Alexandre Bernardino1

  • 1Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.

Frontiers in Robotics and AI
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel closed-loop system for event-based visual-inertial odometry, enhancing feature tracking and pose estimation by integrating event cameras with inertial sensors. The new method improves tracking accuracy and robustness, especially during high-speed motions.

Keywords:
Lie groupsevent cameraspose estimationunscented Kalman filter (UKF)visual inertial odometry (VIO)

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Area of Science:

  • Robotics and Computer Vision
  • Sensor Fusion
  • Event-Based Vision

Background:

  • Conventional cameras struggle with high-speed tracking in Visual Odometry (VO).
  • Event cameras offer high temporal resolution but require new tracking methods.
  • Existing Event-based Kanade-Lucas-Tomasi tracker (EKLT) has limitations in camera motion speed due to local feature registration.

Purpose of the Study:

  • To improve feature tracking and pose estimation in Visual-Inertial Odometry (VIO) using event cameras.
  • To develop a closed-loop system integrating event-based tracking with pose estimation.
  • To enhance robustness and accuracy in high-speed motion scenarios.

Main Methods:

  • Proposed a novel approach expanding on EKLT by fusing event-based tracking with a Visual-Inertial Odometry (VIO) system.
  • Utilized an asynchronous Unscented Kalman Filter (UKF) to combine high-rate IMU data with asynchronous event camera information.
  • Implemented a closed-loop feedback mechanism where pose estimation informs feature tracking, and vice-versa.

Main Results:

  • The proposed method demonstrated improved performance in feature tracking and pose estimation compared to conventional approaches.
  • Evaluated on rotational motions using synthetic and real datasets, confirming the benefits of event-based data.
  • The closed-loop system showed significant improvements over the base EKLT.

Conclusions:

  • This work presents the first fusion of visual and inertial data using event cameras with a UKF and EKLT for pose estimation.
  • The closed-loop approach enhances both feature tracking and pose estimation accuracy.
  • Inertial information aids in tracking features that might otherwise be lost, while feature tracking minimizes inertial drift.