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REVIO: Range- and Event-Based Visual-Inertial Odometry for Bio-Inspired Sensors.

Yingxun Wang1, Bo Shao2, Chongchong Zhang2

  • 1Institute of Unmanned System School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

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PubMed
Summary
This summary is machine-generated.

This study introduces Range and Event-based Visual-Inertial Odometry (REVIO) for Unmanned Aerial Vehicles (UAVs). REVIO enhances localization accuracy in challenging dynamic scenes by fusing event data with range information, reducing errors significantly.

Keywords:
event camerarange sensorsensor fusionvisual-inertial odometry (VIO)

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Area of Science:

  • Robotics and Computer Vision
  • Sensor Fusion for Navigation

Background:

  • Visual-inertial odometry (VIO) is crucial for Unmanned Aerial Vehicles (UAVs) and robotics but suffers from motion drift and blur in dynamic environments.
  • Standard cameras struggle with sharp brightness changes and fast motion, degrading image quality and localization accuracy.
  • Event cameras, inspired by biological vision, excel in high-dynamic-range scenes, offering a potential solution.

Purpose of the Study:

  • To develop a novel range and event-based visual-inertial odometry (REVIO) system.
  • To improve the accuracy and robustness of UAV localization in challenging visual conditions.
  • To leverage the advantages of event cameras and range data for enhanced navigation.

Main Methods:

  • Proposed an event-based visual-inertial odometry (EVIO) utilizing sliding window nonlinear optimization.
  • Developed REVIO by integrating EVIO with range measurements to generate clearer event images.
  • Incorporated additional range constraints into the optimization framework to refine position estimation.

Main Results:

  • REVIO demonstrated reduced localization errors by nearly 29% compared to EVIO in handheld experiments.
  • REVIO achieved almost 28% error reduction compared to VINS-Mono in flight experiments.
  • The proposed method shows superior performance in fast-motion and high-dynamic scenes.

Conclusions:

  • REVIO significantly enhances localization accuracy for UAVs in challenging dynamic environments.
  • Fusing event camera data with range information is an effective strategy for robust VIO.
  • The developed system offers a promising advancement for autonomous navigation in robotics and UAV applications.