Multi-Sensor Track-to-Track Association and Spatial Registration Algorithm under Incomplete Measurements

2021 ◽  
pp. 1-1
Author(s):  
Jun Wang ◽  
Yajun Zeng ◽  
Shaoming Wei ◽  
Zixiang Wei ◽  
Qinchen Wu ◽  
...  
Keyword(s):  
Registration Algorithm ◽  
Spatial Registration ◽  
Track Association

scholarly journals Superimposing holograms on real world objects using HoloLens 2 and its depth camera

2021 ◽  
Vol 7 (1) ◽  
pp. 111-115
Author(s):  
Felix von Haxthausen ◽  
Yenjung Chen ◽  
Floris Ernst
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Initial Step ◽  
Depth Camera ◽  
Visual Guidance ◽  
Outlier Removal ◽  
Surgical Interventions ◽  
Registration Algorithm ◽  
Spatial Registration ◽  
Initial Placement

Abstract Augmented Reality glasses such as HoloLens 2 may provide visual guidance during surgical interventions. To superimpose the holograms on real world objects (RWO), for instance a patient, spatial registration is required. In this work, we propose an approach to automatically register a hologram to the according RWO. To this end, the framework utilizes the depth camera of HoloLens 2 to acquire the point cloud (PC) of the RWO. A novel and recently published PC registration algorithm allows to register the PC of the RWO and the hologram after a rough initial placement without any need for pre-processing or outlier removal. The approach is evaluated by measuring displacements between certain known positions of the hologram and the RWO. The first metric relies on measuring points using an optically tracked stylus while the second is based on visually perceived positions. The median displacements were 22.3 mm, 35.6 mm, and 13.3 mm for the x-, y-, and z-axes in the first metric and 8.1 mm, 4.3 mm, and 11.9 mm for the second metric. Even though the accuracy is not yet adequate for many surgical interventions, the framework provides an initial step for a convenient marker less registration of holograms to an RWO.

Research on the Application of ICP Algorithm in Spatial Registration of Computer-Aided Surgery

2013 ◽  
Vol 380-384 ◽  
pp. 4061-4066
Author(s):  
Yun Tao Wei
Keyword(s):  
Reliable Method ◽  
Surface Profile ◽  
Singular Value ◽  
Canny Operator ◽  
Iterative Search ◽  
Registration Algorithm ◽  
Spatial Registration ◽  
Key Points ◽  
Value Decomposition ◽  
Multiple Step

This paper discusses the surface profile method based on image registration, and illustrates the principle and key points of registration algorithm based on surface profile. Canny operator is adopted to extract the surface contour and method based on quaternion and the singular value decomposition is used for space registration of the corresponding points, as well as the closest iterative search (ICP). In order to improve the registration speed, we adopt multiple step iteration method, which has been proved to be a reliable method according to the experiment.

Research on the Topology Preservation of the Demons Non-rigid Registration Algorithm

2010 ◽  
Vol 36 (1) ◽  
pp. 179-183
Author(s):  
Xiang-Bo LIN ◽  
Tian-Shuang QIU ◽  
Su RUAN ◽  
NICOLIER Frédéric
Keyword(s):  
Topology Preservation ◽  
Rigid Registration ◽  
Registration Algorithm

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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