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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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VIO-GO: optimizing event-based SLAM parameters for robust performance in high dynamic range scenarios.

Saber Sakhrieh1, Abhilasha Singh1, Jinane Mounsef1

  • 1Electrical Engineering and Computing Sciences Department, Rochester Institute of Technology, Dubai, United Arab Emirates.

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Summary

This study enhances Visual Inertial Odometry (VIO) for Industry 4.0 robotics using event cameras and a novel VIO-Gradient-based Optimization (VIO-GO) method. VIO-GO significantly improves localization accuracy in challenging industrial settings.

Keywords:
batch gradient descentdynamic and low-light environmentsedge imageevent SLAMoptimizationvisual inertial odometry

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Sensor Fusion

Background:

  • Industry 4.0 robotics face challenges in dynamic, low-light environments.
  • Existing Visual Inertial Odometry (VIO) systems struggle with reliability in these conditions.
  • Event cameras offer potential for improved sensing in challenging industrial settings.

Purpose of the Study:

  • To enhance VIO systems for robust performance in dynamic and low-light Industry 4.0 environments.
  • To introduce a novel optimization method for Event Simultaneous Localization and Mapping (SLAM) parameters.
  • To enable precise and reliable robotic localization and mapping in complex industrial scenarios.

Main Methods:

  • Integration of bio-inspired event cameras with conventional video and inertial data for state estimation.
  • Development of a VIO-Gradient-based Optimization (VIO-GO) method using Batch Gradient Descent (BGD).
  • Automated parameter tuning for Event SLAM using motion-compensated images to represent event data.

Main Results:

  • A 60% improvement in Mean Position Error (MPE) compared to fixed-parameter methods was achieved.
  • VIO-GO consistently identified optimal parameters for precise VIO performance.
  • A 24% reduction in MPE was observed with increased parameter complexity (VIO-GO8 vs. VIO-GO2).

Conclusions:

  • The proposed VIO-GO method effectively optimizes Event SLAM parameters for practical applications.
  • The enhanced VIO system demonstrates robust and precise localization capabilities in challenging industrial environments.
  • This approach is scalable and suitable for adaptive robotic systems in Industry 4.0.