Fast and Robust Bin-picking System for DenselyPiled Industrial Objects*

Jiaxin Guo, Lian Fu, Mingkai Jia, Kaijun Wang, Shan LiuState Key Laboratory of Industrial Control Technology,

College of Control Science and EngineeringZhejiang University

Hangzhou, Zhejiang, 310027, ChinaEmail{jiaxinguo, lian.fu, mingkaijia, kjwang, sliu}@zju.edu.cn

Abstract—Objects grasping, also known as the bin-picking,is one of the most common tasks faced by industrial robots.While much work has been done in related topics, graspingrandomly piled objects still remains a challenge because muchof the existing work either lacks in robustness or costs toomuch resource. In this paper, a fast and robust bin-pickingsystem is developed to grasp densely piled objects adaptively andsafely. The proposed system starts with point cloud segmentationusing improved density-based spatial clustering of applicationswith noise (DBSCAN) algorithm, improved by combining theregion growing algorithm as well as using Octree to speedup the calculation. The system then uses principle componentanalysis (PCA) for coarse registration and for fine registration,the iterative closest point (ICP) algorithm is used. We proposegrasp risk score (GRS) to evaluate each object by the collisionprobability, the stability of object and the whole pile’s stability.Through real test with Anno robot, our method is verified to beadvanced in speed and robustness.

Index Terms—Bin-picking, Robot Arm, DBSCAN, Registra-tion, Point Cloud

I. INTRODUCTION

Industry has seen a rapid growth of robots application.Humdrum works, such as bin-picking, met the first waveof robots in job. Traditional bin-picking robots have limitedability sensing the environment, making it hard to meet the re-quirements of the complicated real-situation in plants. Recentstudies focus on improving visual approaches of robots. Pointcloud, with its richness in 3D information, is born to be amongthe best choices for object sensing. Generally, bin-pickingproblems are solved by a common pipeline, which basicallycontains plane segmentation, correspondence grouping, coarseregistration and fine registration.

Previous researches about pipeline usually focus on somespecific procedures. Random sample consensus (RANSAC)works well in solving plane segmentation problems, andsome more modified algorithms are proposed recently. Bas-tian Oehler et al. present a method that can segment 3Dpoint clouds into planar components efficiently with multi-resolution, which combines the Hough transform with robustRANSAC [1]. Lin Li et al. propose an improved RANSACmethod to avoid spurious planes for 3D point-cloud plane seg-mentation on the basis of Normal Distribution Transformation

*This work is supported by Key Research and Development Program ofZhejiang Province of China under Grant 2020C01114.

(NDT) cells [2]. Credit to Xian-Feng Han et al., filter algo-rithms are categorized into four classifications [3], statistical-based [4] [5], neighborhood-based [6] [7], projection-based [8]and PDEs-based filtering [9] [10]. Sample consensus initialalignment (SAC-IA) is one of the basic coarse registrationapproaches, with different modified versions specifically de-signed for different situations [11] [12]. Besides, S Zhang etal. use frequency domain point cloud registration based onthe Fourier transform to detect the side data of the whole part[13]. For fine registration, a few new approaches are proposedrecently. Weimin Li et al. propose a modified iterative closestpoint (ICP) algorithm with high speed [14]. Brayan S Zapata-Impata et al. propose a method to find the best pair of thethree-dimensional grasping points for the unknown objectswith a partial view. In order to perform grasps autonomously,the robot needs to know where to place the robotic handto ensure stable grasps. [15]. For object pose estimation,modified algorithms like adaptive threshold for bin-pickingare proposed [16].This pipeline as a whole has also beencovered by a handful of researches. Dirk Buchholz et al. showan applicable solution for the bin-picking problem, focusingon robustness against noise and object occlusions [17] [18].Ales Pochyly et al. present a functional case study, whichconcentrates on the bin-picking system on the basis of themodified revolving vision system, in which they admit itremains hard to implement in industry environment and givea few analysis [19]. While above studies are verified undersimulation, there are some papers give pretty results using realrobot. Carlos Martinez et al. prove their pick-policy and basicvisual system to be robust using ABB IRB2400 robot, and givea practical test method [20]. In addition to eye-to-hand robots,eye-in-hand is also capable to deal with bin-picking problemaccording to Wen-Chung Chang’s vision-based robotic bin-picking system in which CCP approach is proposed [21]. Somepatents are also applied, for example, Kye-Kyung Kim’s patentabout bin-picking system using top-down camera with bin-picking box [22].

However, though much work has been done in bin-picking,regarding the problem of randomly-piled-objects bin-pickingproblem which is a more general scenario in real industryapplication, only a few papers have been published. Oneexample is inward-region-growing-based accurate partitioning

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of closely stacked objects for bin-picking proposed by ZaixingHe et al. Focusing on segmentation of piled objects, they giveno result on real-robot [23].

We make two main contributions in this paper:• An Improved density-based spatial clustering of applica-

tions with noise (DBSCAN) segmentation algorithm isdesigned.

• An efficient and adaptive grasp-policy evaluation functionis proposed.

The rest of the article is organized as follows. Section 2presents an overview of our point-cloud-based bin-pickingalgorithm. In Section 3, our methods are described in detail.And then in Section 4, results and discussion of experimentsare illustrated. Conclusions are drawn in Section 5.

II. OVERVIEW

Fig. 1. The schematic diagram of our approach.

The goal of our research is to develop a fast bin-picking sys-tem for grasping dense and stacked objects adaptively, safelyand fast. Here we outline the given information, the potentialchallenges and the desired capabilities of the proposed method.

Our system is expected to get depth images from RGB-D camera and convert it into 2.5D point cloud for poseestimation. 2.5D point cloud has insufficient dimensionalityfor the single viewpoint of camera. As we focus on graspinggeneral industrial objects like three-way-pipes etc. which havesimilar textures and features, the objects’ computer-aideddesign models with their 3D point cloud can be assumed aspriori knowledge. Simple as their shapes are, it becomes a bigchallenge to estimate their 6-DoF pose accurately when theyare randomly and densely piled. In that case, some traditionalsegmentation methods are not suitable and robust for thisscene, some feature-based registration methods are accuratebut slow and some deep learning methods are robust but havetoo much calculating and time cost.

Our approach is shown in Fig.1. We propose a DBSCAN-based point cloud segmentation algorithm improved throughcombining the Region Growing algorithm and using Octree

to speed up the calculation. Our segmentation algorithm isverified to perform well in dense point cloud segmentationand noise point cloud filtering. To reduce cost in featurescalculation, we use principle component analysis (PCA) toestimate the principle component of the segmented point cloudfor coarse registration, then for fine registration, ICP algorithmis used. For the safety goal, we focus on a novel policy ofgrasping for the objects to avoid the collision while improvethe grasping efficiency. We consider three factors to evaluatethe collision probability, the stability of object and the wholesystem. According to the factors, we define the grasp riskscore (GRS) to evaluate each object. From the rank of GRS,we get the optimal sequence of grasping. In the end of agrasping round, the program will check if the objects movedby the change of segmented centroids. The program will doregistration and calculate GRS again only when a significantdisplacement has occurred, otherwise will continue graspingwithout data process to improve efficiency.

III. METHODS

A. Preprocessing

In our approach, after using RGB-D camera to get pointcloud of grasping area, it is necessary to preprocess the pointclouds so that we can remove the noise and get the pointcloud of target objects. The implementation procedure ofpreprocessing is as following:• Convert the depth image to point cloud.• Extract the grasping area and remove the noise by using

the passthrough filter.• Use voxel grid filter to downsample, that is to reduce the

number of point cloud but retain the detailed informationof original point cloud.

• Use RANSAC to remove the plane of the point cloudrapidly [24].

Fig. 2. Preprocessing result.

The preprocessing result is shown in Fig.2.

B. Improved DBSCAN algorithm for segmentation

There has been many point cloud segmentation algorithms,like Region growing, Euclidean Clustering, RANSAC and soon. However, they are not robust for piled and dense objects,which have arbitrary poses and overlapping point cloud. Toovercome this problem, the paper proposes an improved DB-SCAN algorithm for point cloud segmentation of stacked and

dense objects, combining Region Growing with DBSCAN andusing Octree to improve the efficiency of algorithm.

DBSCAN algorithm is a simple and effective clusteringalgorithm on the basis of density. It can seek out arbitraryshape clustering as well as remove noise without any priorknowledge [25]. There are 2 parameters required for DB-SCAN, Eps and MinPts. Firstly, DBSCAN evaluates thepoints density by defining a cluster as the maximum setof points whose neighbor points are within Eps. Secondly,MinPts is the minimum number of neighbors of the corepoints forming the clusters. The border points are containedin the Eps-neighborhood of the other core points. The noisepoints are the points that not belonging to any cluster.

Here are some important definitions of DBSCAN.Definition 1: (directly density-reachable) It is directly

density-reachable between a pair of points p and q wrt. Eps,MinPts if:

p ∈ NEps(q) (1)|NEps(q)| ≥MinPts (2)

Directly density-reachable is symmetric for pairs of corepoints, but not for the pair of a core point and a border point.

Definition 2: (density-reachable) It is density-reachablebetween a pair of points p and q wrt. Eps and MinPts if thereis a chain of points p1, p2, ..., pn, pn = p, p1 = q, in whichpoint pi+1 is directly density-reachable from pi.

The density-reachability is typically extended from thedirect density-reachability. The relationship between them istransitive but not symmetric.

Definition 3: (density-connected) It is density-connectedbetween a pair of points and q wrt. Eps and MinPts if thereis a point o, from which both p and q are density-reachablewrt. Eps and MinPts.

As a symmetric relation, density-connectivity is also reflex-ive for density-reachable points .

Definition 4: (cluster) Let D represent for a dataset ofpoints, of which the non-empty cluster C wrt. Eps and MinPtsis a subset that meets the conditions below:

1)∀p, q: if p ∈ C and q is density-reachable from p wrt.Eps and MinPts, then q ∈ C.(Maximality)

2)∀p, q ∈ C: p is density-connected to q wrt. Eps andMinPts.(Connectivity)

For cluster finding, DBSCAN will start at a point p, which isarbitrarily selected and all points wrt. Eps and MinPts density-reachable from p are retrieved. For the core point p, DBSCANgenerates a cluster wrt. Eps and MinPts, and for the borderpoint p, it will visit the next point.

There are some deficiencies of DBSCAN. First, it costsa lot when finding the neighbor points to form clusters.It’s necessary to speed up the calculation to improve theefficiency. Second, DBSCAN algorithm only merges the Eps-neighborhood points of core points, which means lots ofborder points are lost in this proceeding. It causes a loss ofthe objects’ edge and some detailed point cloud, which maylead to a bad registration of following proceedings.

Improved DBSCAN:1) Use Octree to speed up:To improve the efficiency of DBSCAN for finding the

nearest neighbors, it is necessary to use relevant algorithmsto speed up the calculation. As a dynamic partition algorithm,K-d tree is efficient and has been widely implemented forhandling point cloud. However, all of the child nodes shouldbe retrieved to traverse the child nodes where the 3D boundarysatisfies a positional query. Inspired by Soohee Han’s work[26], Octree is adopted in this paper.

Octree is a tree used to describe three-dimensional space.Each node of octree represents a cube volume element, andeach node has eight child nodes. When using Octree for pointcloud, the 3D boundary is divided into eight octants, whichwill be further subdivided recursively only when they bearpoints within themselves until the sequence reaches a giventhreshold value depth. In Octree, only one child node in eachdepth is traversed for the 3D boundary of each node is knownby positional query. And a leaf node can be advantageouslyretrieved to improve the index efficiency.

By using Octree to find the nearest points, the accuracy andefficiency of DBSCAN are improved to deal with 3D pointcloud data.

2) Combine with Region Growing algorithmTo avoid the loss of some details and bounds, we present

a approach combining the DBSCAN with Region Growingalgorithm [27]. Firstly, Region Growing sorts the points bytheir curvature value and picks points with minimum curvaturevalue as seeds for the region’s growth. Secondly, the RegionGrowing algorithm measures local geometry by fitting a planeto some point’s neighborhood to find surface normals. Us-ing the residual of plane fitting, the local curvature is alsoapproximated. Two parameters θth and rth are involved inthe method, which mean curvature threshold and smoothnessthreshold. According to the two parameters, Region Growingfocuses on using the points’ normal and residuals to grouppoints with smooth surfaces.

DBSCAN usually results in over segmented point cloudsand lost borders, but Region Growing tends to extend formore points from the growing seeds. To enhance the DBSCANalgorithm for solving the problems, we combine the RegionGrowing method with DBSCAN algorithm, which can takeDBSCAN clusters as region growing seeds to start growing.Instead of only merging the clusters of core points into one,we also merge the border point cluster to the core point clusteraccording to some rules:• The border point and core point are directly density-

reachable.• The Eps neighborhoods of two points have the most in

common.• The normal of a point is estimated using it’s neighboring

points to fit a plane. The curve features and normal of twopofts’ Eps neighborhood can be matched, which meansthe two clusters have similar surface properties.

The algorithm proceeding is in algorithm 1. The seg-mentation result is shown in Fig.3. According to Fig.3, for

Fig. 3. Segmentation results of different algorithms. The centroids of eachcluster are calculated and shown as red points.

Algorithm 1 Improved DBSCAN1: input: Octree depth d, Eps, MinPts, Point cloud = {P},

Point normals = {N}, Point curvatures = {c}, smooththreshold θth, curvaturethreshold cth

2: Use Octree to voxelize the point cloud with depth lessthan d.

3: Use Octree to find Eps-neighborhood of each points basedon Euclidean distance.

4: According to MinPts, find the core points and borderpoints, exclude the noises.

5: Merge the clusters according to Definition 4.6: For border points, find their directly density-reachable

core points.7: Find the cluster of the core points which have most in

intersaction.8: Calculate the normals and curvature of core point cluster

and border point cluster, if less than cth and θth, mergethem into new clusters.

Euclidean Clustering and Region Growing, the segmentationcould not separate the dense and overlapping point cloud ofeach object accurately, and some noise could not be removed;for DBSCAN, the segmentation separates well but loses detailsand borders of objects; for our method, the above objectswhich have relatively complete point cloud are segmented,and some bottom objects and noise point cloud are filtered.

C. Registration

After segmentation of piled objects, a fast and robust reg-istration method is necessary for estimate the pose of objects.This paper fulfills the coarse registration with PCA and usesICP to further improve the accuracy.

Coarse Registration: To reduce the dimensions of thedata set, PCA algorithm retains the greatest feature of thedata which contribute to variance at most. For point setsP (x1, x2, ...xn), xi is n-dimensions data, and the algorithmcalculate the mean and variance [28], as shown in 3 and 4.

x =1

n

n∑i=1

xi (3)

covP =1

n

n∑i=1

(xi − x)(xi − x)T (4)

The principle component of the point cloud P can be figuredout from the co-variance matrix’s feature vectors covP . For thepoint cloud data which has three dimension, here we denoteoverlinex as the coordinate system’s origin, and use the threefeature vectors as the XYZ axes which represent the principlecomponent of the data. The coarse registration can be achievedby adjusting the coordinate systems between the model pointcloud and the target point cloud.

Fine Registration: Point-to-point ICP are applied for fineregistration to estimate the pose of object, thus improvingthe accuracy of registration. ICP needs an iterative initialtransformation matrix T, which can be set by the resultof coarse registration. Then point-to-point ICP updates thetransformation T through minimizing the objective functionE(T ):

E(T ) =∑

(p,q)∈D

||p− Tq||2 (5)

The registration results are shown in Fig. 4.

Fig. 4. Registration results. The principle components are shown as threeaxises.

D. Policy of grasping

Grasp policy involves grasp order, grasp position, graspangle and gripper opening width. Inspired by Kentaro Kozai’swork [29], we propose Grasp Risk Score(GRS) to optimizeabove parameters in case to avoid collision and improve thegrasping efficiency.

1) Grasp order selection: We consider three factors ingrasp order selection. Pself (i) represents the unstability ofobject i itself. We use Pgrasp(i) to describe the possibilityof collision while grasping object i. Psystem(i) tells thecontribution to the system’s stability of object i, that is, ifthe system would tend to collapse without object i, then thePsystem(i) would be pretty large in value.

We define the Grasp Risk Score(GRS) of object i inequation 6.

GRS(i) = (αPself (i) + βPgrasp(i)) · Psystem(i) (6)

with α, β to be tuned.The GRS expresses the possibility of object i to be safely

picked up by formulating the Grasp Policy as a function ofthe grasping order. The optimal grasping parameter i can bedetermined by maximizing GRS, as in equation 7.

i = argmini∈P

GRS(N) (7)

with N being the number of objects.Pself (i) expresses the unstability of the object i itself. We

consider the height of the object i’s barycenter zoi and theprincipal axis angle θi to be the main factors that determinethe stability of the object. Generally, a large zoi means objecti is on the top of the pile, while a small one means it’s lyingon the ground, which is obviously more stable. And a slantprincipal axis means it tends to topple over. Pself (i) is definedas equation 8

Pself (i) = k1 · zt + k2 · sin θ (8)

with k1 and k2 to be tuned.Pgrasp(i) represents the possibility of collision while grasp-

ing object i. Since the grasp parameters are uncertain, we justcalculate the density of objects around object i by using KD-Tree to find the distance of K nearest neighbors as an estimate.Denote the centroid position of object i as (xi, yi, zi), thecentroid position of other object as (xj , yj , zj). Pgrasp(i) isdefined as formula 9.

Pgrasp(i) =1

K

∑i∈K{(xi−xj)2+(yi−yj)2+(zi−zj)2} (9)

Psystem(i) expresses object i’s contribution to the system’sstability. An object can count a lot if it’s supporting orpropping against another object. As point cloud data is gainedfrom an overhead camera, it contains only the information ofthe top layer of the object pile, thus above situations wouldbe presented as overlap. Through advanced segmentation andregistration, the dependencies information could be gained.Replace the origin point cloud Qi with the template pointcloud Qi introduced during the registration. For target objectOtarget, we have formula 10.

Psystem(i) =

0 threshold < COUNTi∈N

(Qtarget ∩ Qi)

1 threshold ≥ COUNTi∈N

(Qtarget ∩ Qi)

(10)with N being the number of detected objects andCOUNT (Q) meaning count the points’ number of pointcloud Q.

2) Grasp position determination: As Brayan S et al. pro-posed, the grasp point should on the object’s principle axislaxis [15]. Besides, the rules exists that the smaller the distancefrom the center of mass the better, and the larger the distancefrom the nearby object to the barycenter the better. Denote thedistance from the lower endpoint of the principle axis to thegrasp point as x, the distance between the barycenter and the

TABLE ISEGMENTATION RESULTS.

Segmentation Accuracy Runtime (s)

Euclidean Clustering 73% 0.112Region Growing 47% 0.339

DBSCAN 80% 0.354Our Method 95% 0.084

TABLE IIEXPERIMENTS RESULTS.

Segmentation Registration Success Rate Collision Rate

Euclidean Clustering SCAIA+ICP 78% 28%Euclidean Clustering NDT+ICP 71% 31%

Region Growing SCAIA+ICP 61% 40%Region Growing NDT+ICP 56% 35%

Our Method 92% 7%

grasp point as de(x), the distance between the grasp point andthe nearest object as dn(x). The grasp point’s position x isdetermined by formula 11.

x = argminx

k3 · de(x) + k4 · dn(x) (11)

k3 and k4 to be tuned.

IV. EXPERIMENTS AND RESULTS

We evaluate the proposed algorithm’s performance and andcompare with conventional algorithms using Anno robotic armwith Robot operating system (ROS), which provides solid andconvenient testing environment for the operation of the arm.

After validating the algorithms on the ”Randomly PiledIndustrial Objects” dataset, the results of segmentation arelisted in Table I. Improved DBSCAN is capable of segmentingpoint cloud of dense and randomly piled industrial objects,reducing noise and removing some bottom objects.

Our grasp system is deployed to an Anno robotic arm witha kinect V2 camera, as shown in fig. 5. Two main factors,grasp-success-rate and collapse-rate, works to evaluate theperformance of our method. Former is the ratio of the numberof objects successfully moved out of the box to the totalnumber of the objects, while the latter is the times of collisionsto the total times of grasping. The results of the experimentwith the proposed and comparative pipelines are listed in TableII. Among the approaches involved in the experiments, ourmethod gains the best performance.

Photograph of the working robot is shown in Fig.5. Thegesture of the robot when it’s grasping the object in corner isparticularly presented in Fig.6.

V. CONCLUSION

In this paper, a pipeline for grasping random piled and denseobjects fast, adaptively and safely is proposed. Our methodsstart at segmentate point cloud with modified DBSCAN-basedalgorithm, which is improved by combining the Region Grow-ing algorithm and using Octree to speed up the calculation.

Fig. 5. Grasp object on top. Fig. 6. Grasp object in corner

Then, for coarse registration, PCA algorithm is used in ourmethod and for fine registration, ICP is applied. We proposedGrasp Risk Score to evaluate each object by the collisionprobability, the stability of object and the whole system’sstability. Through real test with Anno robot, our method isverified to be advanced in speed and robustness.

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