This paper focuses on 6D pose estimation for weakly textured targets from RGB-D images. A 6D pose estimation algorithm (DOPE++) based on a deep neural network for weakly textured objects is proposed to solve the poor real-time pose estimation and low recognition efficiency in the robot grasping process of parts with weak texture. More specifically, we first introduce the depthwise separable convolution operation to lighten the original deep object pose estimation (DOPE) network structure to improve the network operation speed. Second, an attention mechanism is introduced to improve network accuracy. In response to the low recognition efficiency of the original DOPE network for parts with occlusion relationships and the false recognition problem in recognizing parts with scales that are too large or too small, a random mask local processing method and a multiscale fusion pose estimation module are proposed. The results show that our proposed DOPE++ network improves the real-time performance of 6D pose estimation and enhances the recognition of parts at different scales without loss of accuracy. To address the problem of a single background representation of the part pose estimation dataset, a virtual dataset is constructed for data expansion to form a hybrid dataset.
It's a type of wheeled robot based on the AT89S52 MCU, through the body and infrared emission source docking judgment target position, the differential signal to control the motor speed to improve the direction; used ultrasonic obstacle avoidance module continuously detect the front object distance; used the error compensation principle of probability and statistics on the mechanical arm debugging and correcting, so that the system may realize to the target automatically crawl.
ASM is a statistical model applied to match contours of non-rigid object. The actual contour may much different from the initial contour and the result is likely to converge to an error contour. Kalman filter is adopted to track the current frame for the prediction and acts as the initial state of the ASM, and then applies the ASM to correct the contour of the object. Experimental results show that the method proposed in this paper allows the model to converge to the target contour quickly and accurately. It has good stability and robustness.
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