Research on pixel-level grabbing detection algorithm based on RGB-D
Aiming at the grabbing problems of robot arm,such as random object attitude,irregular shape and complex grabbing environment,a pixel-level grabbing detection method based on RGB-D is proposed.Firstly,the Cornell dataset is re-labeled based on pixel-level,and grabbing tags are generated.Then,a convolutional neural network for multiple residual extraction (CRE-Net)is proposed to enhance the effect of network feature extraction.Finally,a simulation grabbing system is built to verify the algorithm.The experimental results show that the precision of pose detection reaches 93.99％.In adversarial grabbing the success rate of single object grabbing is 94.0％,and the success rate of multi object grabbing is 73.3％.
plane grabbingdeep learningposition and attitude estimationresidual network
万方数据
