  Vision-based grasp estimation is an essential part of robotic manipulation
tasks in the real world. Existing planar grasp estimation algorithms have been
demonstrated to work well in relatively simple scenes. But when it comes to
complex scenes, such as cluttered scenes with messy backgrounds and moving
objects, the algorithms from previous works are prone to generate inaccurate
and unstable grasping contact points. In this work, we first study the existing
planar grasp estimation algorithms and analyze the related challenges in
complex scenes. Secondly, we design a Pixel-wise Efficient Grasp Generation
Network (PEGG-Net) to tackle the problem of grasping in complex scenes.
PEGG-Net can achieve improved state-of-the-art performance on the Cornell
dataset (98.9%) and second-best performance on the Jacquard dataset (93.8%),
outperforming other existing algorithms without the introduction of complex
structures. Thirdly, PEGG-Net could operate in a closed-loop manner for added
robustness in dynamic environments using position-based visual servoing (PBVS).
Finally, we conduct real-world experiments on static, dynamic, and cluttered
objects in different complex scenes. The results show that our proposed network
achieves a high success rate in grasping irregular objects, household objects,
and workshop tools. To benefit the community, our trained model and
supplementary materials are available at https://github.com/HZWang96/PEGG-Net.
