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一个RGB-D立体摄像机的户外特征和几何感知错误建模,用于安全相关的障碍物检测

Pierluigi Rossi¹, Elisa Cioccolo¹, Maurizio Cutini²

¹Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

立体摄像机对于农业安全至关重要,但它们的户外性能需要量化. 这项研究开发了一个模型来预测和纠正深度错误,以便在农场环境中更好地检测障碍物.

科学领域:

机器人和自动化机器人和自动化
计算机视觉计算机视觉
农业工程农业工程

背景情况:

立体摄像头 (深度摄像头或RGB-D摄像头) 对于机械设备中的障碍物检测和导航至关重要.
它们的户外表现,特别是在不同光线下在中长距离下,并没有得到充分的记录.
这对于农业机械来说至关重要,以检测工人并防止碰撞.

研究的目的:

在现实的农场环境中量化立体声摄像机的户外性能.
开发一个模型来预测和纠正深度错误.
在农业安全应用中对Intel RealSense D455进行基准测试.

主要方法:

建立了一个现场协议,以测试从4m到16m的距离上的立体相机.
在户外环境中使用1平方米平面目标,采用多种照明.
测试包括在相机的视野 (FoV) 中改变目标的位置,并调整相机的预设.
差异表面被安装为深度偏差模型作为距离,光线和FoV位置的函数.

主要成果:

开发了一个模型,以很好的精度预测深度误差 (RMSE:0.46-0.64 m,MAE:0.40-0.51 m).
该模型有效地纠正基于距离,照明和FoV的系统偏差.
结果显示了对传感器进行基准测试和改善安全系统的潜力.

结论:

开发的模型提供了一种可靠的方法来评估在农业环境中立体相机的性能.
准确的深度错误预测支持安全关键感知系统的开发.
这项工作可以对各种传感器和现场条件进行复制和基准测试.

关键词:

在RGB-D中使用RGB-D.农业农业农业农业深度摄像机深度摄像机障碍物检测障碍物检测障碍物检测障碍物检测在安全方面,安全是安全的.

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