TY - GEN
T1 - Depth-based object detection using hierarchical fragment matching method
AU - Haghighi, Reza
AU - Rasouli, Mahdi
AU - Ahmed, Syeda Mariam
AU - Tan, Kim Pong
AU - Al-Mamun, Abdullah
AU - Chew, Chee-Meng
PY - 2018
Y1 - 2018
N2 - Identifying a workpiece in industrial processes using depth sensors has received increasing attention over the past few years. However, this is a challenging task particularly when the object is large or cluttered. In these scenarios, captured point clouds do not provide sufficient information to detect the object. To address this issue, we present a hierarchical fragment matching method for 3D object detection and pose estimation. We build a library of object fragments by scanning the object from different viewpoints. A descriptor, named Clustered Centerpoint Feature Histogram (CCFH), is proposed to compute the features for each fragment. The proposed method aims to enhance the robustness of the existing Clustered Viewpoint Feature Histogram (CVFH) descriptor. Subsequently, an Extreme Learning Machine (ELM) classifier is applied to identify the matched segments between the scene and the library of fragments. Finally, the pose of the object in the scene is estimated using the matched segments. Unlike existing approaches that require the CAD model of the object or pre-registration process, the proposed method directly use the scanned point clouds of the object. The experimental results are presented to illustrate the performance of the proposed method.
AB - Identifying a workpiece in industrial processes using depth sensors has received increasing attention over the past few years. However, this is a challenging task particularly when the object is large or cluttered. In these scenarios, captured point clouds do not provide sufficient information to detect the object. To address this issue, we present a hierarchical fragment matching method for 3D object detection and pose estimation. We build a library of object fragments by scanning the object from different viewpoints. A descriptor, named Clustered Centerpoint Feature Histogram (CCFH), is proposed to compute the features for each fragment. The proposed method aims to enhance the robustness of the existing Clustered Viewpoint Feature Histogram (CVFH) descriptor. Subsequently, an Extreme Learning Machine (ELM) classifier is applied to identify the matched segments between the scene and the library of fragments. Finally, the pose of the object in the scene is estimated using the matched segments. Unlike existing approaches that require the CAD model of the object or pre-registration process, the proposed method directly use the scanned point clouds of the object. The experimental results are presented to illustrate the performance of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85059987277&partnerID=8YFLogxK
U2 - 10.1109/COASE.2018.8560427
DO - 10.1109/COASE.2018.8560427
M3 - Conference proceeding contribution
AN - SCOPUS:85059987277
SN - 9781538635940
SP - 780
EP - 785
BT - 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
T2 - 14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Y2 - 20 August 2018 through 24 August 2018
ER -