No abstract

No abstract

The human body is often in a partially visible state during rescue at the disaster site. At the same time, when the human body is grasped, the camera located on the actuator at the end of the robotic arm cannot fully bring the human body into the field of view. The existing human pose estimation algorithms do not perform well in this situation, and a joint detection method that can estimate the complete human pose through partial human body information and can perform data refresh in real time is required. We use the occlusion part classification module to extract the weight vector, re-weight it and the joint features extracted from AlphaPose, and add human body geometric constraints when generating 3D human body joints to obtain more accurate 3D human body joint positions. Finally, the robot is successfully used to complete the human grasping experiment, which verifies the effectiveness of the algorithm.