运动平台双IMU与视觉组合姿态测量算法

Fused Pose Measurement Algorithm Based on Double IMU and Vision Relative to a Moving Platform

快速、准确地获取被测物体的姿态信息,对于航空航天、工业生产等领域都具有重要意义。相较于单IMU测量物体相对于惯性坐标系的姿态,在未知平台的运动状态的情况下,采用双IMU实现物体相对于运动平台坐标系的姿态测量。将IMU测量速度快、短期精度高的特点与视觉测量误差不随时间发散的特点相结合,研究运动平台上双IMU与视觉组合姿态测量算法。提出了一种多速率自适应拓展卡尔曼滤波(MAEKF)算法来融合IMU与视觉的测量结果。采用正交双矢量标定法与q-method实现惯性测量中的坐标系标定。实验证明,该组合姿态测量算法能快速、准确地测量被测物体相对于运动平台的姿态。

Obtaining the attitude of a measured object quickly and accurately has great significance in aerospace and industry manufacturing fields.Generally,single IMU can be used to measure the attitude of a measured object relative for an inertial coordinate system.To obtain the attitude relative to a moving coordinate system,double IMU scheme is employed.To measure the attitude of a measured object relative to a moving platform,an algorithm based on double IMU and vision is discussed.IMU has high update frequency and short-term precision,the error of vision dosen’t diverge with the time.The discussed algorithm combines the advantages of IMU and vision.IMU and vision measurement results are fused by a multi-speed adaptive extend Kalman filter(MAEKF).Orthogonal double-vector calibration and q-method are used to calibrate the coordinate system in inertial measurement.Experimental results show that the fusion algorithm can measure objec