基于Keypoint RCNN改进模型的物体抓取检测算法

Object grasp detection algorithm based on improved Keypoint RCNN model

机器人抓取在工业中的应用有两个难点:如何准确地检测可抓取物体,以及如何从检测出的多个物体中选择最优抓取目标。本文在Keypoint RCNN模型中引入同方差不确定性学习各损失的权重,并在特征提取器中加入注意力模块,构成了Keypoint RCNN改进模型。基于改进模型提出了两阶段物体抓取检测算法,第一阶段用模型预测物体掩码和关键点,第二阶段用掩码和关键点计算物体的抓取描述和重合度,重合度表示抓取时的碰撞程度,根据重合度可以从多个可抓取物体中选择最优抓取目标。对照实验证明,相较原模型,Keypoint RCNN改进模型在目标检测、实例分割、关键点检测上的性能均有提高,在自建数据集上的平均精度分别为85.15%、79.66%、86.63%,机器人抓取实验证明抓取检测算法能够准确计算物体的抓取描述、选择最优抓取,引导机器人无碰撞地抓取目标。

There are two difficulties in the application of robot grasping in industry. How to detect the graspable object accurately and how to select the optimized grasp target among the detected multiple objects. In this paper the homoscedastic uncertainty is introduced into Keypoint RCNN to learn the weights of various losses, the attention modules are integrated into feature extractor, which composes the improved Keypoint RCNN model. A two-stage object grasp detection algorithm is proposed based on the improved Keypoint RCNN model. In the first stage, the improved model is used to predict the masks and keypoints. In the second stage, the masks and keypoints are used to compute the grasp representation and overlap rate of the object, the overlap rate represents the level of collision while grasping. According to the overlap rate, the optimized grasp target can be selected from multiple graspable objects. Comparison experiment indicates that the performances of the improved Keypoint RCNN model are improved in object detection, instance segmentation and keypoint detection compared with those of original model, and the average precisions(AP) on the self-built dataset reach 85.15%, 79.66% and 86.63%, respectively. Robot grasping experiment proves that the proposed grasp detection algorithm can accurately calculate the grasp representation, select the optimized grasp and guide the robot to grasp the target with collision-free grasp.