  In this paper, we propose a novel edge and corner detection algorithm for an
unorganized point cloud. Our edge detection method classifies a query point as
an edge point by evaluating the distribution of local neighboring points around
the query point. The proposed technique has been tested on generic items such
as dragons, bunnies, and coffee cups from the Stanford 3D scanning repository.
The proposed technique can be directly applied to real and unprocessed point
cloud data of random clutter of objects. To demonstrate the proposed
technique's efficacy, we compare it to the other solutions for 3D edge
extractions in an unorganized point cloud data. We observed that the proposed
method could handle the raw and noisy data with little variations in parameters
compared to other methods. We also extend the algorithm to estimate the 6D pose
of known objects in the presence of dense clutter while handling multiple
instances of the object. The overall approach is tested for a warehouse
application, where an actual UR5 robot manipulator is used for robotic pick and
place operations in an autonomous mode.
