  Objects grasping, also known as the bin-picking, is one of the most common
tasks faced by industrial robots. While much work has been done in related
topics, grasping randomly piled objects still remains a challenge because much
of the existing work either lack robustness or costs too much resource. In this
paper, we develop a fast and robust bin-picking system for grasping densely
piled objects adaptively and safely. The proposed system starts with point
cloud segmentation using improved density-based spatial clustering of
application with noise (DBSCAN) algorithm, which is improved by combining the
region growing algorithm and using Octree to speed up the calculation. The
system then uses principle component analysis (PCA) for coarse registration and
iterative closest point (ICP) for fine registration. We propose a grasp risk
score (GRS) to evaluate each object by the collision probability, the stability
of the object, and the whole pile's stability. Through real tests with the Anno
robot, our method is verified to be advanced in speed and robustness.
