Reshaping Field of View and Resolution with Segmented Reflectors: Bridging the Gap Between Rotating and Solid-State...

Area of Science:

• Optics and Photonics
• Robotics and Autonomous Systems
• Sensor Technology

Background:

• Rotating Light Detection And Ranging (LiDAR) systems often suffer from data loss due to occlusion and limited field of view (FOV).
• Discarded data reduces the effectiveness of LiDAR for critical applications like autonomous driving.

Purpose of the Study:

• To introduce and validate a novel segmented reflector for rotating LiDAR systems.
• To address data loss issues and enhance LiDAR performance by optimizing the FOV.

Main Methods:

• Optical simulations using Zemax OpticStudio to assess performance impacts.
• Pattern simulations to configure radially reshaped FOVs for density and coverage.
• Prototype development and experimental validation measuring intensity, Euclidean error, and standard deviation.

Main Results:

• Optical simulations showed only a 3.9% reduction in LiDAR range with the reflector.
• Pattern simulations demonstrated configurable FOVs for maximizing point cloud density or coverage.
• Experimental validation confirmed no significant performance degradation, with a drastic increase in usable measurements.

Conclusions:

• The segmented reflector effectively redirects the entire LiDAR FOV, mitigating data loss.
• The design enhances object revisit rate, resolution, classification range, and robustness against environmental challenges.
• This technology enables robust super-resolution for long-range rotating LiDARs, crucial for high-speed autonomous driving.