Pose error analysis method based on a single circular feature
Pattern Recognition, ISSN: 0031-3203, Vol: 129, Page: 108726
2022
- 6Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations6
- Citation Indexes6
- CrossRef1
Article Description
The measurement accuracy of pose parameters based on a single circular feature depends not only on the accuracy of camera calibration and feature extraction but also on the relative pose of the feature and camera—different poses correspond to different error transmission coefficients. To obtain the relationship between measurement errors and pose parameters, we propose an error analysis method based on geometric interpretation. The method characterises measurement error by the sensitivity the imaging feature has to the variation of pose parameters. In addition, the method can be extended to the error analysis work of other coplanar features' pose measurement algorithms. We conducted simulations on measurement errors of pose parameters under different poses, and the results show that the error distribution of pose parameters is in good agreement with the theoretical analysis. Moreover, we propose a method for judging and optimising outliers, and experimental results show the feasibility of this method.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0031320322002072; http://dx.doi.org/10.1016/j.patcog.2022.108726; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85129305926&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0031320322002072; https://dx.doi.org/10.1016/j.patcog.2022.108726
Elsevier BV
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