  Robotic bin packing is very challenging, especially when considering
practical needs such as object variety and packing compactness. This paper
presents SDF-Pack, a new approach based on signed distance field (SDF) to model
the geometric condition of objects in a container and compute the object
placement locations and packing orders for achieving a more compact bin
packing. Our method adopts a truncated SDF representation to localize the
computation, and based on it, we formulate the SDF minimization heuristic to
find optimized placements to compactly pack objects with the existing ones. To
further improve space utilization, if the packing sequence is controllable, our
method can suggest which object to be packed next. Experimental results on a
large variety of everyday objects show that our method can consistently achieve
higher packing compactness over 1,000 packing cases, enabling us to pack more
objects into the container, compared with the existing heuristics under various
packing settings.
