多传感器移动机器人校准准确度和结构稳健性的动态验证
Yang Liu1,2, Ximin Cui1, Shenghong Fan2
1School of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究引入了一种新的方法,用于在动态行驶过程中验证多传感器移动机器人的校准准确度和结构稳定性. 这种新的方法确保了可靠的传感器性能,以实现强大的机器人操作.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 传感器系统 传感器系统
- 校准和验证 校准和验证
背景情况:
- 高精度校准和结构强度对于多传感器移动机器人的可靠性至关重要.
- 现有的验证方法对于动态旅行场景是不够的.
研究的目的:
- 为多传感器移动机器人校准准确度和结构稳固性提出一种新的验证方法.
- 为了使移动机器人测试阶段的动态评估.
主要方法:
- 开发了一个"地面模拟场 (GSSF) -移动机器人 -光电发射站 (PTS) "机制.
- 静态GSSF以真北点确定;PTS用于实时移动车辆姿势测量.
- 动态传感器用极线对齐和点云投影/叠加位置进行姿势测量和评估.
主要成果:
- 该方法动态评估各种传感器 (Navcam,Hazcam,多谱,TOF,LiDAR) 的校准准确度和结构稳定性.
- 双极直线对齐评估了对双眼相机的相对方向校准.
- 点云投射和叠加评估了与车身相对绝对校准和强度.
结论:
- 拟议的方法为动态验证多传感器移动机器人的可靠手段.
- 这有助于在复杂的环境中确保移动机器人的健康和强大的运行.
相关概念视频
Stereotype Content Model
14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K
Data Validation
160
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
160


