Visual-inertial navigation systems are powerful in their ability to accurately estimate localization of mobile systems within complex environments that preclude the use of global navigation satellite systems. However, these navigation systems are reliant on accurate and up-to-date temporospatial calibrations of the sensors being used. As such, online estimators for these parameters are useful in resilient systems. This paper presents an extension to existing Kalman Filter based frameworks for estimating and calibrating the extrinsic parameters of multi-camera IMU systems. In addition to extending the filter framework to include multiple camera sensors, the measurement model was reformulated to make use of measurement data that is typically made available in fiducial detection software. A secondary filter layer was used to estimate time translation parameters without closed-loop feedback of sensor data. Experimental calibration results, including the use of cameras with non-overlapping fields of view, were used to validate the stability and accuracy of the filter formulation when compared to offline methods. Finally the generalized filter code has been open-sourced and is available online.
翻译：视觉-神经导航系统在准确估计移动系统在无法使用全球导航卫星系统的复杂环境中的本地化能力方面十分强大,能够准确估计移动系统在无法使用全球导航卫星系统的复杂环境中的位置化,然而,这些导航系统依赖于对所使用传感器的准确和最新的热空间校准,因此,这些参数的在线估计器在具有复原力的系统中是有用的。本文件介绍了现有基于Kalman过滤器的框架的延伸,用以估计和校准多相机IMU系统的外部参数。除了将过滤框架扩大到包括多个相机传感器之外,还重新制定了测量模型,以便利用通常在纤维检测软件中提供的测量数据。使用二级过滤层来估计时间转换参数,而不对传感器数据进行闭路反馈。实验校准结果,包括使用不重叠的摄像场,用于验证过滤器配方与离线方法相比的稳定性和准确性。最后,通用过滤码是开放源的,可在网上查阅。
相关内容
微信扫码咨询专知VIP会员