Attitude Measurement of Excavator Working Device Based on Multipoint Markers
Aiming at the problem that target in attitude of excavator working device is easily obscured by soil contamination and the attitude measurement fails due to background environment impact,a multi-point mark based attitude measurement method for excavator working device is proposed,which uses an RGB camera and YOLOv3 deep learning algorithm to realize attitude measurement of excavator working device without any sensors.We have set the key point markings which are different from the background environment.Then,capture a large amount of working device image.Label the key point markings in the captured images and form the training data sets.Further,obtain the key point marking recognition model by applying YOLOv3 learning algorithm on the training data.In addition,the three-dimensional information of key point markings can be restored according to the constraint relationship between key points and camera imaging principle.Therefore,we could compute the attitude angle of corresponding bar.The results show that:this attitude measurement system can solve the problems in attitude measurement,and the instantaneous deviation of the excavator working device attitude measurement is within±2°and the average deviation is with-in±1°to meet the attitude angle feedback in visual servo;at the same time,the average processing time of each frame image of the measurement system is108.26ms,which meets the real-time measurement conditions.
万方数据
维普
