Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms.
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http://dx.doi.org/10.3390/s23136112 | DOI Listing |
Sci Rep
January 2025
Divisions of Physical Therapy and Rehabilitation Science, Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, MN, 55455, USA.
OrthoFusion, an intuitive super-resolution algorithm, is presented in this study to enhance the spatial resolution of clinical CT volumes. The efficacy of OrthoFusion is evaluated, relative to high-resolution CT volumes (ground truth), by assessing image volume and derived bone morphological similarity, as well as its performance in specific applications in 2D-3D registration tasks. Results demonstrate that OrthoFusion significantly reduced segmentation time, while improving structural similarity of bone images and relative accuracy of derived bone model geometries.
View Article and Find Full Text PDFComput Med Imaging Graph
December 2024
School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, Beijing, PR China; Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, 450000, Henan, PR China. Electronic address:
In skull base surgery, the method of using a probe to draw or 3D scanners to acquire intraoperative facial point clouds for spatial registration presents several issues. Manual manipulation results in inefficiency and poor consistency. Traditional registration algorithms based on point clouds are highly dependent on the initial pose.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning.
View Article and Find Full Text PDFEntropy (Basel)
November 2024
China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China.
In industrial robotic arm gripping operations within disordered environments, the loss of physical information on the object's surface is often caused by changes such as varying lighting conditions, weak surface textures, and sensor noise. This leads to inaccurate object detection and pose estimation information. A method for industrial object pose estimation using point cloud data is proposed to improve pose estimation accuracy.
View Article and Find Full Text PDFVector Borne Zoonotic Dis
January 2025
Infectious Diseases and Vaccine Programs Branch, Public Health Agency of Canada, Saint-Hyacinthe, Canada.
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