3D matching by combining CAD model and computer vision for autonomous bin picking

Hanh, Le Duc; Hieu, Khuong Thanh Gia

doi:10.1007/s12008-021-00762-4

Cited by 17 publications

(3 citation statements)

References 20 publications

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“…These algorithms try to address two deficiencies in the acquired 3D point clouds: removing outliers and reducing noise. The distinction between these is subject to interpretation, but for point clouds obtained from objects for which computer-aided design (CAD) models are known, the distinction can be based on the distance between a datapoint and the closest vertex, or face, of the correctly oriented CAD model [ 18 ]. For such cases, we consider a 3D point as noisy when its distance to the CAD model is smaller than

3

, where

is the residual error from fitting the CAD model to the 3D point cloud using, for example, the iterative closest point (ICP) or any other minimization technique.…”

Section: Related Work On Filtering 3d Point Cloudsmentioning

confidence: 99%

Filtering Organized 3D Point Clouds for Bin Picking Applications

Franaszek,

Rachakonda,

Saidi

2024

Applied Sciences

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In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.

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3

, where

is the residual error from fitting the CAD model to the 3D point cloud using, for example, the iterative closest point (ICP) or any other minimization technique.…”

Section: Related Work On Filtering 3d Point Cloudsmentioning

confidence: 99%

Filtering Organized 3D Point Clouds for Bin Picking Applications

Franaszek,

Rachakonda,

Saidi

2024

Applied Sciences

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“…With the penetration of intelligent services into modern production environments (see figure 5), many enterprises are transforming digitally to integrate digital services and network physical production systems (CPPS) (11). In industrial production activities, to improve production efficiency and reduce the burden of labour, mechanical methods, ultrasonic and electrical methods have gradually been replaced by non-contact optical visual inspection over the past decade (12). It is faster, more sustainable, and accurate and avoids human interference, making it familiar in current inspection systems.…”

Section: Development Of Automated Inspection Systemsmentioning

confidence: 99%

Enhancing the quality inspection process in the food manufacturing industry through automation

Lin

Rahman

Maropoulos

2022

2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)

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“…On the other hand, the gripper-agnostic works in this category involve an object-centric approach [1]. One type of objectcentric approach uses 3D CAD model of the target object [43] which is not suitable for novel objects whose CAD model is not available. Another type of solution in this category is to apply a category-agnostic instance segmentation [1], [12],…”

Section: Bin-pickingmentioning

confidence: 99%

Towards Object Agnostic and Robust 4-DoF Table-Top Grasping

Raj

Kumar

Sanap³

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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This paper addresses category-agnostic instance segmentation for robotic manipulation, focusing on segmenting objects independent of their class to enable versatile applications like bin-picking in dynamic environments. Existing methods often lack generalizability and object-specific information, leading to grasp failures. We present a novel approach leveraging objectcentric instance segmentation and simulation-based training for effective transfer to real-world scenarios. Notably, our strategy overcomes challenges posed by noisy depth sensors, enhancing the reliability of learning. Our solution accommodates transparent and semi-transparent objects which are historically difficult for depth-based grasping methods. Contributions include domain randomization for successful transfer, our collected dataset for warehouse applications, and an integrated framework for efficient bin-picking. Our trained instance segmentation model achieves state-of-the-art performance over WISDOM public benchmark [1] and also over the custom-created dataset. In a real-world challenging bin-picking setup our bin-picking framework method achieves 98% accuracy for opaque objects and 97% accuracy for non-opaque objects, outperforming the state-of-theart baselines with a greater margin.

show abstract

3D matching by combining CAD model and computer vision for autonomous bin picking

Cited by 17 publications

References 20 publications

Filtering Organized 3D Point Clouds for Bin Picking Applications

Filtering Organized 3D Point Clouds for Bin Picking Applications

Enhancing the quality inspection process in the food manufacturing industry through automation

Towards Object Agnostic and Robust 4-DoF Table-Top Grasping

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