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

Published

February 12, 2025

Author(s)

Colin Schaefer, Zeid Kootbally, Vinh Nguyen

Abstract

Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR), cameras, and radars, to ensure their robustness in driving scenarios and weather conditions. The data from these sensors are fused together to generate maps of the surrounding environment and provide information for the detection and tracking of objects. Hence, evaluation methods are necessary to compare existing and future sensor systems through quantifiable measurements given the wide range of sensor models and design choices. This paper presents a proposed evaluation method to compare colored point clouds, a common fused data type, among two LiDAR-camera fusion systems and a stereo camera setup. The evaluation approach uses a test artifact measured by the fusion system's colored point cloud through the spread, area coverage, and color difference of the colored points within the computed space. The test results showed the evaluation approach was able to rank the sensor fusion systems based on its metrics and complement the experimental observations. The proposed evaluation methodology is therefore suitable towards the comparison of generated colored point clouds by sensor fusion systems.

Citation

Sensors

Volume

25

Issue

4

Pub Type

Journals

Download Paper

https://doi.org/10.3390/s25041111

Local Download

Keywords

Sensor Evaluation, LiDAR, Camera, Sensor Fusion, Automated Driving, Metrology

Citation

Schaefer, C. , Kootbally, Z. and Nguyen, V. (2025), Quality Evaluation for Colored Point Clouds Produced by Autonomous Vehicle Sensor Fusion Systems, Sensors, [online], https://doi.org/10.3390/s25041111, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958271 (Accessed April 10, 2025)