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Quality Evaluation for Colored Point Clouds Produced by Autonomous Vehicle Sensor Fusion Systems.

Colin Schaefer¹, Zeid Kootbally², Vinh Nguyen¹

¹Department of Mechanical and Aerospace Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This summary is machine-generated.

This study introduces a new method to evaluate sensor fusion systems for autonomous vehicles (AVs). The approach quantifies colored point cloud data, enabling objective comparison of LiDAR-camera and stereo camera setups.

Keywords:

LiDAR automated driving systems camera colored point clouds metrology sensor fusion

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Area of Science:

Robotics and Computer Vision
Autonomous Vehicle Perception Systems

Background:

Autonomous vehicles (AVs) rely on sensor fusion (e.g., LiDAR, cameras) for robust environmental perception.
Quantifiable evaluation methods are crucial for comparing diverse sensor fusion systems and design choices.

Purpose of the Study:

To present a novel evaluation method for comparing colored point clouds generated by different sensor fusion systems.
To assess LiDAR-camera fusion systems against a stereo camera setup using quantifiable metrics.

Main Methods:

Developed an evaluation approach using a test artifact measured by colored point clouds.
Metrics include point cloud spread, area coverage, and color difference.
Compared two LiDAR-camera fusion systems and one stereo camera system.

Main Results:

The evaluation method successfully ranked the performance of the sensor fusion systems.
The metrics provided quantifiable data that complemented experimental observations.
Demonstrated the suitability of the approach for comparing fused point cloud data.

Conclusions:

The proposed evaluation methodology is effective for comparing colored point clouds from sensor fusion systems.
This method aids in selecting and optimizing sensor configurations for autonomous driving.
Facilitates objective assessment of perception system performance under various conditions.