IMU-RGBD camera 3D pose estimation and extrinsic calibration: Observability analysis and consistency improvement
IMU-RGBD camera 3D pose estimation and extrinsic calibration: Observability analysis and consistency improvement
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IMU-RGBD相机3D位姿估计和外参标定：可观测性分析和一致性改进
DOI：
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发表时间：
2013
期刊：
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通讯作者：
S. Roumeliotis
中科院分区：
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作者：
C. Guo;S. Roumeliotis
在本文中，我们解决了相对于 RGBD 传感器对惯性测量单元 (IMU) 进行外部校准的问题。特别是，我们研究了非线性 IMU-RGBD 校准系统的可观测性，并证明了在给定单点特征观测的情况下，校准参数是可观测的。此外，我们表明系统有四个不可观测的方向，对应于全局平移和绕重力矢量的旋转。根据可观测性分析的结果，我们设计了一种一致性改进的、基于可观测性约束 (OC) 扩展卡尔曼滤波器 (EKF) 的估计器，用于校准传感器对，同时跟踪其姿态并创建环境的 3D 地图。最后，我们验证了可观测性分析的主要结果，并评估了 OC-EKF 估计器在模拟和实验中的性能。
In this paper, we address the problem of extrinsically calibrating an inertial measurement unit (IMU) with respect to an RGBD sensor. In particular, we study the observability of the nonlinear IMU-RGBD calibration system and prove that the calibration parameters are observable given observations to a single point feature. Moreover, we show that the system has four unobservable directions corresponding to the global translation and rotations about the gravity vector. Based on the results of the observability analysis, we design a consistency-improved, observability constrained (OC) extended Kalman filter (EKF)-based estimator for calibrating the sensor pair while at the same time tracking its pose and creating a 3D map of the environment. Finally, we validate the key findings of the observability analysis and assess the performance of the OC-EKF estimator both in simulation and experimentally.