SpecTrack dataset

DOI

Precision pose detection is increasingly demanded in fields such as personal fabrication, Virtual Reality (VR), and robotics due to its critical role in ensuring accurate positioning information. However, conventional vision-based systems used in these systems often struggle with achieving high precision and accuracy, particularly when dealing with complex environments or fast-moving objects. To address these limitations, we investigate Laser Speckle Imaging (LSI), an emerging optical tracking method that offers promising potential for improving pose estimation accuracy. Specifically, our proposed LSI-Based Tracking (SpecTrack) leverages the captures from a lensless camera and a retro-reflector marker with a coded aperture to achieve multi-axis rotational pose estimation with high precision. Our extensive trials using our in-house built testbed have shown that SpecTrack achieves an accuracy of 0.31° (std=0.43°), significantly outperforming state-of-the-art approaches and improving accuracy up to 200%.

Identifier
DOI https://doi.org/10.5522/04/27232944.v1
Related Identifier HasPart https://ndownloader.figshare.com/files/49807620
Related Identifier HasPart https://ndownloader.figshare.com/files/49807629
Related Identifier HasPart https://ndownloader.figshare.com/files/49807704
Related Identifier HasPart https://ndownloader.figshare.com/files/49807794
Metadata Access https://api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:figshare.com:article/27232944
Provenance
Creator Chen, Ziyang; Aksit, Kaan
Publisher University College London UCL
Contributor Figshare
Publication Year 2024
Rights https://creativecommons.org/licenses/by-nc-nd/4.0/
OpenAccess true
Contact researchdatarepository(at)ucl.ac.uk
Representation
Language English
Resource Type Dataset
Discipline Other