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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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ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter.

Fabien Colonnier1,2, Luca Della Vedova3, Garrick Orchard1

  • 1Temasek Laboratories, National University of Singapore, Singapore 117411, Singapore.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an event-based algorithm using an Extended Kalman Filter for accurate 6-Degree of Freedom sensor pose estimation. The system achieves low-latency, event-by-event pose updates, enabling robust performance even during rapid motion.

Keywords:
computer visionevent-based sensorextended Kalman filterstructureless measurement modelvisual odometry

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Area of Science:

  • Robotics
  • Computer Vision
  • Embedded Systems

Background:

  • Event-based vision sensors offer low-latency, low-computational cost sensing for embedded applications.
  • Accurate sensor pose estimation is crucial for many robotic and vision tasks.

Purpose of the Study:

  • To develop and evaluate an event-based algorithm for 6-Degree of Freedom (6DoF) sensor pose estimation.
  • To demonstrate the algorithm's performance with single and multiple sensors in various environments.

Main Methods:

  • An Extended Kalman Filter (EKF) is employed for event-by-event pose updates.
  • The algorithm is tested on recorded data from diverse scenes (planar, natural) with a handheld sensor.
  • An extension to a multi-sensor setup and integration with a mapping algorithm are presented.

Main Results:

  • The algorithm achieves low-latency pose estimation (worst-case < 2 μs on FPGA).
  • Accurate pose estimation is demonstrated for rapid motions up to 2.7 m/s.
  • Multi-sensor configurations show improved performance and enable 3D scene point cloud updates.

Conclusions:

  • The proposed event-based EKF algorithm provides efficient and accurate 6DoF sensor pose estimation.
  • The system is suitable for embedded applications and robust to dynamic movements.
  • Multi-sensor integration enhances performance and expands potential applications in visual odometry and 3D mapping.