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Track alignment inspection based on machine vision and inertial system

EasyChair Preprint no. 1498

6 pagesDate: September 12, 2019


Track alignment inspection is one of the most important method to ensure safe transportation. Due to the cumulative error of the gyroscope and the accelerometer, the conventional inertial measurement has low accuracy under the low speed. In order to solve this problem, a novel inspected method for railway space curve based on multi-sensors fusion of machine vision and inertial measurement is proposed. By using extended Kalman filter, the fusion of the computer vision and inertia information is obtained. Moreover, the inspected performance of the proposed method is investigated by experiment. Compared with previous methods in other works, the results demonstrate that the new method has higher accuracy. Furthermore, it is found that the measurement accuracy of the proposed method has improved nearly 10 times.

Keyphrases: inertial measurement, machine vision, Multi-sensors fusion Model, Railway space curve.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Lele Peng and Huiling Zhang and Ruyan Huang and Shubin Zheng},
  title = {Track alignment inspection based on machine vision and inertial system},
  howpublished = {EasyChair Preprint no. 1498},

  year = {EasyChair, 2019}}
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