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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Georeferencing of Laser Scanner-Based Kinematic Multi-Sensor Systems in the Context of Iterated Extended Kalman Filters Using Geometrical Constraints
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Georeferencing of Laser Scanner-Based Kinematic Multi-Sensor Systems in the Context of Iterated Extended Kalman Filters Using Geometrical Constraints

机译：基于几何约束的迭代扩展卡尔曼滤波器中基于激光扫描仪的运动学多传感器系统的地理配准

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摘要

Georeferencing is an indispensable necessity regarding operating with kinematic multi-sensor systems (MSS) in various indoor and outdoor areas. Information from object space combined with various types of prior information (e.g., geometrical constraints) are beneficial especially in challenging environments where common solutions for pose estimation (e.g., global navigation satellite system or external tracking by a total station) are inapplicable, unreliable or inaccurate. Consequently, an iterated extended Kalman filter is used and a general georeferencing approach by means of recursive state estimation is introduced. This approach is open to several types of observation inputs and can deal with (non)linear systems and measurement models. The capability of using both explicit and implicit formulations of the relation between states and observations, and the consideration of (non)linear equality and inequality state constraints is a special feature. The framework presented is evaluated by an indoor kinematic MSS based on a terrestrial laser scanner. The focus here is on the impact of several different combinations of applied state constraints and the dependencies of two classes of inertial measurement units (IMU). The results presented are based on real measurement data combined with simulated IMU measurements.
机译：对于在各种室内和室外区域使用运动学多传感器系统（MSS）进行操作，地理配准是必不可少的。来自对象空间的信息与各种类型的先验信息（例如，几何约束）相结合，在挑战性环境中尤其有益，在这些挑战性环境中，姿态估计的通用解决方案（例如，全球导航卫星系统或全站仪的外部跟踪）不适用，不可靠或不准确。因此，使用了迭代扩展卡尔曼滤波器，并介绍了一种通过递归状态估计的通用地理配准方法。这种方法对几种类型的观测输入开放，并且可以处理（非线性）线性系统和测量模型。使用显式和隐式状态与观测之间关系的能力以及考虑（非线性）等式和不等式状态约束的能力是一个特殊功能。提出的框架是通过基于地面激光扫描仪的室内运动MSS进行评估的。这里的重点是应用状态约束的几种不同组合的影响以及两类惯性测量单元（IMU）的依赖性。给出的结果基于真实的测量数据和模拟的IMU测量值。

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