# A Low-Cost, High-Speed, and Robust Bin Picking System for Factory Automation Enabled by a Non-Stop, Multi-View, and Active Vision Scheme

arXiv: https://arxiv.org/abs/2410.00706 · HTML: https://mlsanity.com/paper/2410.00706/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [CosyPose: Consistent multi-view multi-object 6D pose estimation](https://mlsanity.com/paper/2008.08465/) (2020)
- [Intel RealSense Stereoscopic Depth Cameras](https://mlsanity.com/paper/1705.05548/) (2017)
- [Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd](https://mlsanity.com/paper/1512.07506/) (2015)
- [Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge](https://mlsanity.com/paper/1609.09475/) (2016)
- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion](https://mlsanity.com/paper/2004.04336/) (2020)
- [Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image](https://mlsanity.com/paper/2109.06161/) (2021)
- [Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking](https://mlsanity.com/paper/1912.12125/) (2019)
- [ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking](https://mlsanity.com/paper/2105.04112/) (2021)
- [MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network](https://mlsanity.com/paper/2208.01172/) (2022)
- [Real-Time 6D Object Pose Estimation on CPU](https://mlsanity.com/paper/1811.08588/) (2018)
- [6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization](https://mlsanity.com/paper/2210.11554/) (2022)
- [Probabilistic Multi-View Fusion of Active Stereo Depth Maps for Robotic Bin-Picking](https://mlsanity.com/paper/2103.10968/) (2021)
- [Next-Best-View Prediction for Active Stereo Cameras and Highly Reflective Objects](https://mlsanity.com/paper/2202.13263/) (2022)

## Built on by (most cited first, 3 of 3)

- [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025)
- [Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking](https://mlsanity.com/paper/2604.04690/) (2026)
- [RYOPO: Bringing End-to-End Category-Level Object Pose Estimation into Real Time](https://mlsanity.com/paper/2610.03013/) (2026)

## Abstract (arXiv)

Bin picking systems in factory automation usually face robustness issues caused by sparse and noisy 3D data of metallic objects. Utilizing multiple views, especially with a one-shot 3D sensor and "sensor on hand" configuration is getting more popularity due to its effectiveness, flexibility, and low cost. While moving the 3D sensor to acquire multiple views for 3D fusion, joint optimization, or active vision suffers from low-speed issues. That is because sensing is taken as a decoupled module from motion tasks and is not intentionally designed for a bin picking system. To address the problems, we designed a bin picking system, which tightly couples a multi-view, active vision scheme with motion tasks in a "sensor on hand" configuration. It not only speeds up the system by parallelizing the high-speed sensing scheme to the robot place action but also decides the next sensing path to maintain the continuity of the whole picking process. Unlike others focusing only on sensing evaluation, we also evaluated our design by picking experiments on 5 different types of objects without human intervention. Our experiments show the whole sensing scheme can be finished within 1.682 seconds (maximum) on CPU and the average picking complete rate is over 97.75%. Due to the parallelization with robot motion, the sensing scheme accounts for only 0.635 seconds in takt time on average.