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A New Approach to ORB Acceleration Using a Modern Low-Power Microcontroller.

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Summary
This summary is machine-generated.

This study explores using microcontrollers for Oriented FAST and Rotated BRIEF (ORB) feature extraction in visual Simultaneous Location And Mapping (SLAM) systems. While slower than hardware acceleration, it offers a lower-power, cost-effective alternative for specific applications.

Keywords:
ARM microcontrollerORBSLAMlow-power microcontrollervisual odometry

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Area of Science:

  • Computer Vision
  • Robotics
  • Embedded Systems

Background:

  • Feature extraction and description are crucial for visual Simultaneous Location And Mapping (SLAM) systems.
  • Oriented FAST and Rotated BRIEF (ORB) is a common but computationally intensive algorithm used in SLAM.
  • Hardware acceleration (FPGAs, ASICs) is a typical solution but increases costs.

Purpose of the Study:

  • To propose and evaluate a feature extraction and description solution using a low-power microcontroller.
  • To analyze the execution time and power consumption of the ORB algorithm on a microcontroller.
  • To assess the viability of microcontroller-based ORB for SLAM applications.

Main Methods:

  • Implementation of the ORB algorithm on a modern low-power microcontroller.
  • Analysis of ORB execution time varying feature points and internal variables.
  • Measurement of power consumption during ORB execution.

Main Results:

  • ORB execution time reached a maximum of 0.6 seconds for 1241 × 376 resolution images.
  • Power consumption ranged between 30 and 40 milliwatts.
  • The microcontroller-based solution is slower than hardware acceleration but viable for certain applications.

Conclusions:

  • A microcontroller-based ORB implementation offers a lower-power and more economical alternative to hardware acceleration for SLAM.
  • The performance is suitable for applications where real-time constraints are less stringent.
  • Further optimizations can enhance the performance of microcontroller-based ORB for SLAM.