Low Cost Bin Picking Solution for E-Commerce Warehouse Fulfillment Centers
Avnish Gupta, Akash Jadhav, Pradyot VN Korupolu

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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Robot Manipulation and Learning · Robotic Path Planning Algorithms
