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A General Approach to Online Sensor Calibration for Navigation Using Factor Graphs

机译:使用因子图的在线传感器校准的一般方法

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We present a general method for online sensor calibration using factor graphs, which can be applied to a wide range of sensors and parameter types. Our approach refines a particular sensor's calibration parameters using all sensors' measurements recorded in a small period of time at the beginning of a navigation mission, and then uses the refined parameters in the rest of the mission. The parameter estimation module is implemented in our two-tier navigation system which comprises a low-latency Hierarchical Multi-Rate Filter (HMF) and a high-quality Trajectory Smoother/Mapper (TSM). HMF and TSM run in parallel to provide a low-latency and accurate navigation solution. TSM includes the parameter estimation sub-module, which refines the sensor calibration parameters. We present methods for calibrating four types of parameters that are often encountered in a navigation system: wind velocity, compass bias, time offset between system's clock and camera's clock, and step size of pedestrians. In addition, we validated our proposed calibration method using both real aerial and ground data.
机译:我们使用因子图表提供了一般的在线传感器校准,可以应用于各种传感器和参数类型。我们的方法使用在导航任务开头的一小一段时间内记录的所有传感器的测量来精制特定的传感器的校准参数,然后在其余任务中使用精细参数。参数估计模块在我们的二层导航系统中实现,该系统包括低延迟分层多速率滤波器(HMF)和高质量的轨迹更顺畅/映射器(TSM)。 HMF和TSM并行运行,提供低延迟和准确的导航解决方案。 TSM包括参数估计子模块,其改进了传感器校准参数。我们提出了用于校准在导航系统中经常遇到的四种参数的方法:风速,罗盘偏置,系统时钟和相机时钟之间的时间偏移,以及行人的步长。此外,我们验证了我们使用真正的天线和地面数据的建议的校准方法。

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