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.

翻译：摘要：机器人装箱极具挑战性，尤其是在考虑物体多样性与包装紧凑性等实际需求时。本文提出SDF-Pack——一种基于符号距离场（SDF）的新方法，用于建模容器内物体的几何条件，并计算物体放置位置与装箱顺序，以实现更紧凑的装箱。本方法采用截断符号距离场表示来局部化计算，并基于此提出SDF最小化启发式策略，以寻找最优放置方案，使新物体与已有物体紧密排列。为进一步提升空间利用率，若装箱顺序可控，本方法可建议下一个应放入的物体。在大量日常物体的实验结果表明，与现有启发式方法在不同装箱设置下相比，本方法在超过1000个装箱案例中始终能实现更高的包装紧凑性，从而将更多物体装入容器中。