Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
Siang Chen Wei Tang Pengwei Xie Wenming Yang Guijin Wang
Fast and robust object grasping in clutter is a crucial component of
robotics. Most current works resort to the whole observed point cloud for 6-Dof
grasp generation, ignoring the guidance information excavated from global
semantics, thus limiting high-quality grasp generation and real-time
performance. In this work, we show that the widely used heatmaps are
underestimated in the efficiency of 6-Dof grasp generation. Therefore, we
propose an effective local grasp generator combined with grasp heatmaps as
guidance, which infers in a global-to-local semantic-to-point way.
Specifically, Gaussian encoding and the grid-based strategy are applied to
predict grasp heatmaps as guidance to aggregate local points into graspable
regions and provide global semantic information. Further, a novel non-uniform
anchor sampling mechanism is designed to improve grasp accuracy and diversity.
Benefiting from the high-efficiency encoding in the image space and focusing on
points in local graspable regions, our framework can perform high-quality grasp
detection in real-time and achieve state-of-the-art results. In addition, real
robot experiments demonstrate the effectiveness of our method with a success
rate of 94% and a clutter completion rate of 100%. Our code is available at
https://github.com/THU-VCLab/HGGD.