Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm

Lin

2015

Meas. Sci. Technol.

This work proposes a novel approach to segmenting randomly stacked objects in unstructured 3D point clouds, which are acquired by a random-speckle 3D imaging system for the purpose of automated object detection and reconstruction. An innovative algorithm is proposed; it is based on a novel concept of 3D watershed segmentation and the strategies for resolving over-segmentation and under-segmentation problems. Acquired 3D point clouds are first transformed into a corresponding orthogonally projected depth map along the optical imaging axis of the 3D sensor. A 3D watershed algorithm based on the process of distance transformation is then performed to detect the boundary, called the edge dam, between stacked objects and thereby to segment point clouds individually belonging to two stacked objects. Most importantly, an object-matching algorithm is developed to solve the over- and under-segmentation problems that may arise during the watershed segmentation. The feasibility and effectiveness of the method are confirmed experimentally. The results reveal that the proposed method is a fast and effective scheme for the detection and reconstruction of a 3D object in a random stack of such objects. In the experiments, the precision of the segmentation exceeds 95% and the recall exceeds 80%.

Section: Segmentation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Viewpoint-independent 3D object segmentation for randomly stacked objects using optical object detection

Lin

2015

Meas. Sci. Technol.

“…Systems that involve 3-D data processing can overcome the existing difficulties in 2-D digital imaging by relying on both shape and color information of the objects. These systems have recently been integrated with automated machine manipulators for pick and place applications [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Graph-based methods extract the geometric properties of a 3-D shape using a graph, which represents the type and spatial relations between shape components [ 6 , 7 , 12 ]. In these approaches, the topology graph is built using the detected primitive shapes in the scanned scene.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Surface Descriptor for Automated 3-D Object Recognition and Localization

2019

Sensors

This paper presents a novel approach to the automated recognition and localization of 3-D objects. The proposed approach uses 3-D object segmentation to segment randomly stacked objects in an unstructured point cloud. Each segmented object is then represented by a regional area-based descriptor, which measures the distribution of surface area in the oriented bounding box (OBB) of the segmented object. By comparing the estimated descriptor with the template descriptors stored in the database, the object can be recognized. With this approach, the detected object can be matched with the model using the iterative closest point (ICP) algorithm to detect its 3-D location and orientation. Experiments were performed to verify the feasibility and effectiveness of the approach. With the measured point clouds having a spatial resolution of 1.05 mm, the proposed method can achieve both a mean deviation and standard deviation below half of the spatial resolution.

Sub-OBB based object recognition and localization algorithm using range images

Hoang

2016

Meas. Sci. Technol.

This paper presents a novel approach to recognize and estimate pose of the 3D objects in cluttered range images. The key technical breakthrough of the developed approach can enable robust object recognition and localization under undesirable condition such as environmental illumination variation as well as optical occlusion to viewing the object partially. First, the acquired point clouds are segmented into individual object point clouds based on the developed 3D object segmentation for randomly stacked objects. Second, an efficient shape-matching algorithm called Sub-OBB based object recognition by using the proposed oriented bounding box (OBB) regional area-based descriptor is performed to reliably recognize the object. Then, the 3D position and orientation of the object can be roughly estimated by aligning the OBB of segmented object point cloud with OBB of matched point cloud in a database generated from CAD model and 3D virtual camera. To detect accurate pose of the object, the iterative closest point (ICP) algorithm is used to match the object model with the segmented point clouds. From the feasibility test of several scenarios, the developed approach is verified to be feasible for object pose recognition and localization.