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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
Summary
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.
- 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.
