基于互补滤波器和惯性SLAM算法的ROV姿态估计

发布时间：2018-03-05 20:54

  本文选题：ROV　切入点：姿态估计　出处：《哈尔滨工业大学》2017年博士论文　论文类型：学位论文

【摘要】：姿态估计具有广泛的应用,如空中,水下,机器人,导航系统,游戏,工业,增强现实系统等。目前,在该领域的深入研究已经产生了许多完善的估计方法,其中复杂的如卡尔曼滤波,简单的如互补滤波器。一般而言,传统的姿态或角度估计滤波器的计算复杂度较高。为此,研究令人满意的、精确的、计算复杂度低的算法是本文的初衷。因此,为了针对某些应用,给出鲁棒性强、简单、高效的方法,互补滤波器(CF)得到了长足的发展。首先,互补滤波器的最新应用是基于固定增益互补算法(FGCF)和渐变下降的互补算法(GDCF),该方法被用于基于微机电系统(MEMS)的惯性测量单元(IMU)中。这些固定增益估计器分别使用陀螺仪和加速度计进行高低频姿态估计。结合不同的实际应用,通过MPU6050 IMU的仿真和实验验证了GDCF和FGCF的性能。由于在没有辅助传感的情况下使用IMU,两个滤波器的性能仅限于欧拉距离和侧倾角度的姿态估计。两者的估计结果相近,但是,FGCF比GDCF略有优势,其一是具有更高的精度,其二是该方法的两个可调增益能够提供额外的选择。此外,相比于GDCF,FGCF滤波器增益的波动较小。两种算法的计算复杂度几乎相同。其次,本文分别使用FGCF和GDCF算法,以及扩展卡尔曼滤波法,进行MEMS IMU的姿态估计,并比较了估计的结果。基于MPU6050 IMU的仿真和实验数据,使用欧拉角度估计,对估计器的性能进行了评估,评估的依据是均方根误差(RMSE)。此外,通过调整参数进行算法寻优。结果表明,在不考虑计算负荷的前提下,卡尔曼滤波及其变体算法是解决位置和姿态估计问题的标准方法,FGCF和GDCF是解决此问题下的有效方法。结果评估中,EKF的效果最佳,但与CF相比,计算时间更长。与GDCF相比,FGCF有一点优势,部分原因在于FGCF的可调增益能提供更多的选择。再次,FGCF、变增益互补滤波器(VGCF)和扩展卡尔曼滤波器(EKF)是许多应用的有效解决方法,它们具有固定增益,计算复杂度分别为简单、中等和复杂。MEMS IMU互补滤波器的精度,可以在少量计算的前提下,通过改变/切换滤波器增益的方法得到提高。这两种方法都可以有效地用于辅助INS系统,其中寻求较小计算负荷的算法是该应用的主要研究方向。用于姿态估计的GDCF具有固定的增益,其数值不会随系统的动态条件发生改变,这种情况会导致估计的错误。而复杂的算法由于具有较高的计算复杂度,不适用于大多数应用对系统资源的限定。我们提出了模糊优化互补滤波(FTCF)算法来消除误差,并保证最小的计算负荷。所提出的算法与卡尔曼滤波算法进行了比较与评估。结果证明,与GDCF相比,FTCF大大减少了姿态估计的误差。验证了每个动态条件下滤波器增益的调整都在减小姿态估计误差方面发挥了作用。此外,FTCF具有很小的计算成本,但其性能优于GDCF,与复杂的卡尔曼滤波相近。最后,本文基于所提出的惯性SLAM算法,使用IMU的输出数据和声纳观察到的特征来估计潜水器的速度和姿态,估计过程不使用其它诸如GPS等定位系统。惯性SLAM算法是INS和SLAM算法的组合。与EKF-SLAM相比,惯性SLAM的时间复杂度更低。所采用的粒子滤波器仅需使用较少的粒子数就可以达到EKF-SLAM的精度,并具有更快的计算速度。
[Abstract]:Attitude estimation has a wide range of applications, such as air, underwater robot, navigation system, game industry, augmented reality system. At present, research in the field has produced many perfect estimation methods, such as the complex Calman filter, as simple as complementary filter. In general, the calculation of complex filter the high degree of attitude or point of view of the traditional estimation. Therefore, research on satisfactory, accurate, low computational complexity of the algorithm is the original intention of this article. Therefore, in order to give some applications, strong robustness, simple method, high efficiency, complementary filter (CF) has got considerable development. Firstly, the complementary the new application of the filter is fixed gain complementary algorithm (FGCF) based on the gradient descent algorithm and complementary (GDCF), the method is used in microelectromechanical systems (MEMS) based on the inertial measurement unit (IMU). These fixed points gain estimator Don't use gyroscopes and accelerometers, high frequency attitude estimation. Combined with the practical application of different performance, GDCF and FGCF were verified by simulation and experiment of MPU6050 IMU. Due to the use of IMU in the absence of auxiliary sensor case, the performance of the two filters only Euler distance and inclination angle of the estimation results of attitude estimation. Similar, but FGCF GDCF than a slight advantage, one is with higher accuracy, the second is the method of the two adjustable gain can provide additional choices. In addition, compared to the GDCF, small fluctuation FGCF filter gain. Two algorithms for computing complexity is almost the same. Secondly, this paper uses FGCF and the GDCF algorithm, and the extended Calman filter method, MEMS IMU pose estimation, and compare the estimation results. The simulation and experimental data of MPU6050 based on IMU, using Euler angle estimation, the estimator The performance was evaluated on the basis of the assessment is the root mean square error (RMSE). In addition, algorithm optimization by adjusting the parameters. The results show that without considering the computational load, Calman filter and its variants of position and attitude estimation algorithm is used to solve problems of the standard method, FGCF and GDCF are the effective way to solve this problem the results in the assessment of the effect of EKF is best, but compared with CF, the computing time is longer. Compared with GDCF, FGCF has an advantage, in part because the FGCF adjustable gain can provide more choices. Once again, FGCF, variable gain complementary filter (VGCF) and the extended Calman filter (EKF) is effective to solve many application methods, they have a fixed gain, the computational complexity was simple, medium and complex.MEMS IMU complementary filter precision, can be a little calculation, by changing the switch / filter gain Improved. These two methods can be used effectively to assist INS system, which seek smaller computational load algorithm is the main research direction of the application. For attitude estimation GDCF with fixed gain, dynamic condition of its value is not changed with the system, this situation will lead to the error. And complex because the algorithm has high computational complexity, not suitable for most applications to system resources limited. We propose a fuzzy optimization complementary filtering (FTCF) algorithm to eliminate the error, and ensure the minimum computational load. The proposed algorithm and Calman filtering algorithm was compared with the assessment. The results showed that, compared with GDCF, FTCF can greatly reduce the errors of attitude estimation. Results show each dynamic condition filter to adjust the gain in reducing the attitude estimation error has played a role. In addition, FTCF has a very small The computational cost, but its performance is better than GDCF, similar to the Calman filter complex. Finally, the inertial SLAM algorithm based on the output data and sonar observed features using IMU to estimate the vehicle velocity and attitude estimation process, without the use of other such as GPS positioning system. Inertial SLAM algorithm is a combination of INS and the SLAM algorithm. Compared with EKF-SLAM, lower complexity of inertia SLAM time. The number of particles in particle filter only use less can reach the accuracy of EKF-SLAM, and has faster computing speed.

【学位授予单位】：哈尔滨工业大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TN713;TN96
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本文编号：1571841