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Interleaved Sparse-Dense Scanning for Low-Latency Obstacle Detection and 3D Mapping on an Embedded Robotic Platform.

Syed Khubaib Ali1, Ali A Al-Temeemy1,2,3, Pan Cao1

  • 1School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This study introduces an Interleaved Sparse-Dense Scanning method for robots, balancing detailed 3D mapping with fast obstacle detection. This approach improves reaction speed for embedded mobile robots without sacrificing safety.

Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • LiDAR is crucial for robot navigation and mapping.
  • Embedded robots face a trade-off between scan resolution and reaction speed.
  • Dense scans offer detail but are slow; sparse scans are fast but may miss obstacles.

Purpose of the Study:

  • To present an Interleaved Sparse-Dense Scanning method for embedded mobile robots.
  • To balance reactive obstacle avoidance with 3D mapping capabilities.
  • To optimize LiDAR performance on low-cost platforms.

Main Methods:

  • Developed an Interleaved Sparse-Dense Scanning technique for a servo-actuated LiDAR.
  • Integrated a sparse forward scan for obstacle detection within dense mapping sweeps.
Keywords:
3D mappingLiDARdense scanningembedded roboticsobstacle avoidancepoint cloudreactive navigationsparse scanningtime-of-flight sensing

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  • Derived a geometric model linking sensing range, beam spacing, and detectable object width.

    • Main Results:

    • The sparse configuration (7-point scan, 180° FOV) reliably detected obstacles.
    • Decision latency was reduced by 6.2 times compared to dense scans.
    • Achieved a practical balance between obstacle avoidance and 3D mapping.

    Conclusions:

    • The proposed method offers a viable solution for reactive obstacle avoidance on embedded robots.
    • Explicitly defines system timing and safety limits for practical implementation.
    • Enhances the utility of LiDAR for low-cost mobile robotic platforms.