TY - GEN
T1 - A Method for Robust Object Recognition and Pose Estimation of Rigid Body Based on Point Cloud
AU - Zhao, Guiyu
AU - Ma, Hongbin
AU - Jin, Ying
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Object recognition and pose estimation of rigid body are important research directions in the field of both computer vision and machine vision, which has been widely used in robotic arm disorderly grasping, obstacle detection, augmented reality and so on. This paper introduces a method for object recognition and pose estimation of rigid body based on local features of 3D point cloud. A new 3D descriptor (MSG-SHOT) is proposed in the disordered grasping of robot, and only the depth information is used to complete the recognition and pose estimation of the object, which greatly improve the accuracy in the scenes full of clutters and occlusions. Firstly, the adaptive voxel filter based on local resolution is used to realize data reduction and keypoint extraction. Secondly, the MSG-SHOT descriptor is used to complete feature calculating and matching, and the preliminary object recognition and pose estimation of rigid body are completed. Finally, the fast non-maximum suppression algorithm based on point cloud is used to complete the screening of candidate objects. The experimental results show that our method has stability and accuracy, and has robustness to the scenes full of clutters and occlusions, which meets the standard of high-precision grasping of manipulator.
AB - Object recognition and pose estimation of rigid body are important research directions in the field of both computer vision and machine vision, which has been widely used in robotic arm disorderly grasping, obstacle detection, augmented reality and so on. This paper introduces a method for object recognition and pose estimation of rigid body based on local features of 3D point cloud. A new 3D descriptor (MSG-SHOT) is proposed in the disordered grasping of robot, and only the depth information is used to complete the recognition and pose estimation of the object, which greatly improve the accuracy in the scenes full of clutters and occlusions. Firstly, the adaptive voxel filter based on local resolution is used to realize data reduction and keypoint extraction. Secondly, the MSG-SHOT descriptor is used to complete feature calculating and matching, and the preliminary object recognition and pose estimation of rigid body are completed. Finally, the fast non-maximum suppression algorithm based on point cloud is used to complete the screening of candidate objects. The experimental results show that our method has stability and accuracy, and has robustness to the scenes full of clutters and occlusions, which meets the standard of high-precision grasping of manipulator.
KW - 6D pose estimation
KW - Local feature
KW - MSG-SHOT
KW - Object recognition
KW - Point cloud
UR - https://www.scopus.com/pages/publications/85137006925
U2 - 10.1007/978-3-031-13841-6_43
DO - 10.1007/978-3-031-13841-6_43
M3 - Conference contribution
AN - SCOPUS:85137006925
SN - 9783031138409
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 468
EP - 480
BT - Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings
A2 - Liu, Honghai
A2 - Ren, Weihong
A2 - Yin, Zhouping
A2 - Liu, Lianqing
A2 - Jiang, Li
A2 - Gu, Guoying
A2 - Wu, Xinyu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022
Y2 - 1 August 2022 through 3 August 2022
ER -