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This study introduces a 360° mapping and simultaneous localization and mapping (SLAM) algorithm using equirectangular projection. The system efficiently processes fisheye camera images for real-time localization and mapping applications.

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Area of Science:

  • Robotics
  • Computer Vision
  • Geographic Information Systems

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for autonomous systems.
  • Existing SLAM methods often struggle with 360° imagery and real-time performance.
  • Efficient map representation and feature extraction are key challenges in 360° SLAM.

Purpose of the Study:

  • To design a novel 360° map establishment and real-time SLAM algorithm.
  • To enable robust localization and mapping using equirectangular projection images.
  • To optimize computational efficiency for real-time 360° SLAM.

Main Methods:

  • Utilizes dual back-to-back fisheye cameras for 360° image capture.
  • Employs perspective transformation to reduce feature extraction area and computational load.
  • Leverages Oriented Fast and Rotated Brief (ORB) feature points with GPU acceleration for tracking and pose estimation.
  • Implements a flexible 360° binary map with saving, loading, and online updating capabilities.

Main Results:

  • Achieves real-time performance with an average of 20 frames per second (FPS) on an nVidia Jetson TX2.
  • Demonstrates a low accumulated Root Mean Square (RMS) error of 1% over 250 meters.
  • Successfully performs panoramic stitching and blending for dual-fisheye camera inputs.

Conclusions:

  • The proposed algorithm provides an efficient and stable solution for 360° SLAM.
  • The system supports flexible camera configurations and real-time processing on embedded platforms.
  • This work advances the capabilities of autonomous navigation and mapping in 360° environments.