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

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lidar, point cloud, sensor fusion, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR) and cameras, 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 an 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.

Links and sources

• Official / arXiv page
• PDF from original source

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