Multi-Object Grasping Detection With Hierarchical Feature Fusion

Grasping in cluttered and tight scenes is a necessary skill for intelligent robotics to achieve more general application. Such universal robotics can use their perception abilities to visually identify grasps from a stack of objects. However, most existing grasping detection methods based on deep learning just focus on estimating grasping pose with single-layer features. In this paper, we present a novel grasp detection algorithm termed as multi-object grasping detection network, which can utilize hierarchical features to learn object detector and grasping pose estimator simultaneously. The network is mainly composed of two branches: 1) Object detection branch which is based on the single shot multibox detection approach to discriminate object categories and locate object positions by bounding boxes; 2) Grasping pose estimation branch where hierarchical features are fused together to predict grasping position and orientation. To improve grasping detection performance, attention mechanism is employed in hierarchical feature fusion. For evaluating the proposed model, we build a multi-object grasping dataset where every image contains numerous different graspable objects. The extensive experiments demonstrate that the multi-object grasping detection method achieves the state-of-the-art performance on both object detection and grasping pose estimation.

Abstract licence: CC BY-NC-ND

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