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Improving Attitude of a Motion Robotby Fusion of Inertial Gyroscope and Image Rotation

Nazemipour, Ali | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54667 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Manzouri, Mohammad Taghi
  7. Abstract:
  8. Nowadays, the use of MEMS sensors, due to their small size, lightweight and low cost, has been welcomed in systems such as autonomous vehicles. Although the precision of MEMS gyroscopes has been extremely improved, in some aspects, such as stability of bias, they still suffer from some big error sources, like run-to run bias, which determines the sensor price but is not negligible even inexpensive sensors. In addition to the bias, there are a lot of noises in the gyroscope outputs, where ARW is one of the most important ones, which causes failure in real-signals and produces an error in the position and attitude of mobile systems. Due to the fact that run-to-run bias and ARW are stochastic parameters, they have to be removed by utilizing online methods. Most previous noise reduction techniques have problems such as large computational volume, complexity and the need for prior information, as well as the creation of phase lag in the filter output. Also, practical methods for estimating bias gyroscope are limited by static or non-accelerated methods in non-magnetic environments. However, the use of resources such as GPS or Odometer is provided to solve this problem, but these methods also have limitations and drawbacks. Utilizing a novel, fast and efficient vision-based rotation estimation algorithm for ground vehicles, we have developed a visual gyroscope that is used in our sensor fusion system, in order to estimate run-to-run bias of the MEMS gyroscope, accurately. Also in this thesis, a low pass filter based on the alpha-beta filter with a very low computational overhead is proposed to reduce the amount of noise in the output of a MEMS gyroscope sensor. In order to find the optimal filter gain, unlike the usual methods that measure the rate of reduction of the variance of noise, the improvement in the position of the moving vehicle is selected as a criterion, which is a tradeoff between the amount of noise reduction and the phase delay of the filtered signal.In this work, the KITTI database is used to evaluate the proposed algorithm and filtering. According to our experimental results, the proposed algorithm ,and the filtering are capable of estimating bias of the gyroscope after a convergence time of about 6 seconds and denoising raw data of gyroscope to an acceptable level and improving the position of the moving car. By using them the precision of the MEMS gyroscope is improved, so that cheaper sensors can be used for high precision demands
  9. Keywords:
  10. Sensor Fusion ; Visual Gyroscope ; Raw Data Denoising ; Angle Random Walks Error ; Microelectromechanical Systems (MEMS) ; Run-to-Run Bias

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