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SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP

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This study optimizes Iterative Closest Point (ICP) matching for mobile robots. By identifying outliers during self-localization, it speeds up obstacle detection, reducing overall processing time.

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Area of Science:

  • Robotics
  • Computer Vision
  • Computational Geometry

Background:

  • Iterative Closest Point (ICP) is crucial for mobile robot self-localization using LIDAR scans and facility maps.
  • Obstacle detection is typically a separate process after localization, leading to inefficiencies.
  • Identifying discrepancies between scans and maps is key for both localization and safety.

Purpose of the Study:

  • To accelerate obstacle detection in mobile robotics by integrating it with the ICP self-localization process.
  • To develop a computationally optimized ICP algorithm that identifies outliers efficiently.
  • To reduce the overall time for mobile robot navigation tasks.

Main Methods:

  • Adapted a computationally optimized Iterative Closest Point (ICP) algorithm.
  • Integrated outlier detection directly into the ICP matching process.
  • Leveraged pre-calculated ICP parameters for efficient outlier identification.

Main Results:

  • The adapted ICP implementation successfully identifies outliers (discrepant points) between LIDAR scans and the map.
  • Simultaneous self-localization and obstacle detection reduced processing time by 36.7%.
  • The SLAMICP library provides the optimized ICP implementation.

Conclusions:

  • Integrating obstacle detection with ICP matching significantly enhances efficiency in mobile robotics.
  • The adapted ICP algorithm offers a faster and more streamlined approach to navigation safety.
  • This method provides a practical solution for real-time obstacle avoidance in mobile robot applications.