Low Cost Bin Picking Solution for E-Commerce Warehouse Fulfillment Centers

TL;DR

This paper presents a low-cost, sensor-fusion based robotic bin picking system that effectively handles heterogeneous, randomly placed items in e-commerce warehouses, maintaining high accuracy and speed without prior object knowledge.

Contribution

The paper introduces a novel dual sensor fusion algorithm combining RGB and 3D ToF data for robust object segmentation and pose estimation in cluttered bins, adaptable to various object shapes.

Findings

High accuracy in pose estimation regardless of object size, texture, or occlusion

Consistent detection time of 1 second per item

Cost-effective solution suitable for real-world warehouse automation

Abstract

In recent years, the throughput requirements of e-commerce fulfillment warehouses have seen a steep increase. This has resulted in various automation solutions being developed for item picking and movement. In this paper, we address the problem of manipulators picking heterogeneous items placed randomly in a bin. Traditional solutions require that the items be picked to be placed in an orderly manner in the bin and that the exact dimensions of the items be known beforehand. Such solutions do not perform well in the real world since the items in a bin are seldom placed in an orderly manner and new products are added almost every day by e-commerce suppliers. We propose a cost-effective solution that handles both the aforementioned challenges. Our solution comprises of a dual sensor system comprising of a regular RGB camera and a 3D ToF depth sensor. We propose a novel algorithm that fuses data from both these sensors to improve object segmentation while maintaining the accuracy of pose estimation, especially in occluded environments and tightly packed bins. We experimentally verify the performance of our system by picking boxes using an ABB IRB 1200 robot. We also show that our system maintains a high level of accuracy in pose estimation that is independent of the dimensions of the box, texture, occlusion or orientation. We further show that our system is computationally less expensive and maintains a consistent detection time of 1 second. We also discuss how this approach can be easily extended to objects of all shapes.