- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - 3D Point Cloud Coarse Registration based on Convex Hull Refined by ICP and NDT
摘要： Non-rigid registration is a crucial step for many applications such as motion tracking, model retrieval, and object recognition. The accuracy of these applications is highly dependent on the initial position used in registration step. In this paper we propose a novel Convex Hull Aided Coarse Registration refined by two algorithms applied on projected points.Firstly,the proposed approach uses a statistical method to find the best plane that represents each point cloud. Secondly, all the points of each cloud are projected onto the corresponding planes. Then, two convex hulls are extracted from the two projected point sets and then matched optimally. Next, the non-rigid transformation from the reference to the model is robustly estimated through minimizing the distance between the matched point's pairs of the two convex hulls.Finally, this transformation estimation is refined by two methods. The first one is the refinement of coarse registration by Iterative Closest Point (ICP). The second one consists of the refinement of coarse registration by the Normal Distribution Transform (NDT). An experimental study ,carried out on several clouds, shows that the refinement of coarse registration with ICP gives, in the most cases, a better result than refinement with NDT.
关键词： Iterative Closest Point (ICP),Convex Hull,Normal Distribution Transform (NDT),Non rigid registration,3D point cloud,Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29
-
[ACM Press the 2018 2nd International Conference - Barcelona, Spain (2018.08.03-2018.08.05)] Proceedings of the 2018 2nd International Conference on Cloud and Big Data Computing - ICCBDC'18 - Optical Flow Based Pose Estimation
摘要： In this paper, we present a new variant of ICP (iterative closest point) algorithm based on optical flow for finding better 3D point correspondence and computing six-degrees of freedom (6-DOF) pose of the rigid objects. Our approach integrates optical flow algorithm to ICP method, and proposed method gets advantage of higher resolution of the RGB sensor and depth information of the depth sensor. Fusing color and depth sensors improves pose estimation results. We set ground truth with Vicon motion tracker system. And, our approach is tested on a rigid object and compared to ICP method. Results have shown that optical flow and ICP method computes better rigid object pose estimation and lower error against ICP based pose estimation with only depth positions.
关键词： image projection,Iterative Closest Point (ICP),optical flow,Pose estimation
更新于2025-09-23 15:21:21