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This study presents a novel real-time algorithm for robot positioning in dark, GPS-denied environments. The parallel-serial approach fuses LiDAR and IMU data, achieving double the accuracy of existing methods.

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Area of Science:

  • Robotics
  • Sensor Fusion
  • Computer Vision

Background:

  • Autonomous robots require precise positioning in challenging environments like caves and mines.
  • GPS-denied and dark conditions necessitate alternative localization strategies.
  • Existing algorithms often struggle with real-time performance and accuracy trade-offs.

Purpose of the Study:

  • To develop a real-time parallel-serial algorithm for autonomous robot positioning.
  • To improve accuracy and efficiency in GPS-denied, dark environments.
  • To fuse data from Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors.

Main Methods:

  • A novel algorithm applying an Extended Kalman Filter (EKF) to individual LiDAR scan points.
  • Calculating robot location relative to a triangular mesh.
  • Implementing serial, parallel, and parallel-serial versions of the algorithm, leveraging GPU data structures for the latter.

Main Results:

  • The serial and parallel implementations were too slow for real-time applications.
  • The parallel-serial implementation achieved real-time performance.
  • The proposed algorithm demonstrated a two-fold increase in accuracy compared to the Gaussian Mixture Model (GMM) localization algorithm.

Conclusions:

  • The developed parallel-serial algorithm offers a viable solution for real-time robot localization in challenging environments.
  • The fusion of LiDAR and IMU data with an EKF on mesh points significantly enhances positioning accuracy.
  • This approach sets a new benchmark for autonomous navigation in GPS-denied, dark conditions.