Calibration of an IMU-Camera Cluster Using Planar Mirror Reflection and Its Observability Analysis
Calibration of an IMU-Camera Cluster Using Planar Mirror Reflection and Its Observability Analysis
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DOI：
10.1109/tim.2014.2329388
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发表时间：
2015
影响因子：
5.6
通讯作者：
G. Panahandeh;M. Jansson;P. Händel
中科院分区：
文献类型：
--
作者：
G. Panahandeh;M. Jansson;P. Händel
本文描述了一种新颖且低成本的校准方法，用于估计刚性安装在一起的惯性测量单元（IMU）和相机之间的相对变换。通过融合在平面镜前移动的 IMU 相机装置的测量值来执行校准。为了构建视觉观察，我们选择附加到视觉惯性装置的一组关键特征（KF），其中 KF 的 3-D 位置未知。在校准过程中，系统在平面镜前导航，同时视觉传感器观察镜中 KF 的反射，惯性传感器测量系统随时间的线性加速度和旋转速度。我们在本文中的第一个贡献是研究 IMU 相机校准参数的可观测性特性。对于这个视觉惯性标定问题，我们推导了其时变非线性状态空间模型，并使用李导数秩条件检验研究了其可观测性。我们证明了 KF 的校准参数和 3-D 位置是可观察的。作为我们的第二个贡献，我们提出了一种估计校准参数以及 KF 的 3D 位置和所分析系统的动力学的方法。然后在无迹卡尔曼滤波器框架中解决估计问题。我们使用模拟和实验来说明理论分析的结果。所取得的性能表明，我们提出的方法可以方便地用于消费产品，例如智能手机中基于视觉惯性的应用程序，用于定位、3D 重建和监控应用程序。
This paper describes a novel and a low-cost calibration approach to estimate the relative transformation between an inertial measurement unit (IMU) and a camera, which are rigidly mounted together. The calibration is performed by fusing the measurements from the IMU-camera rig moving in front of a planar mirror. To construct the visual observations, we select a set of key features (KFs) attached to the visual inertial rig where the 3-D positions of the KFs are unknown. During calibration, the system is navigating in front of the planar mirror, while the vision sensor observes the reflections of the KFs in the mirror, and the inertial sensor measures the system's linear accelerations and rotational velocities over time. Our first contribution in this paper is studying the observability properties of IMU-camera calibration parameters. For this visual inertial calibration problem, we derive its time-varying nonlinear state-space model and study its observability properties using the Lie derivative rank condition test. We show that the calibration parameters and the 3-D position of the KFs are observable. As our second contribution, we propose an approach for estimating the calibration parameters along with the 3-D position of the KFs and the dynamics of the analyzed system. The estimation problem is then solved in the unscented Kalman filter framework. We illustrate the findings of our theoretical analysis using both simulations and experiments. The achieved performance indicates that our proposed method can conveniently be used in consumer products like visual inertial-based applications in smartphones for localization, 3-D reconstruction, and surveillance applications.