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Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology

Overview of attention for article published in Frontiers in Robotics and AI, February 2024
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Title
Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology
Published in
Frontiers in Robotics and AI, February 2024
DOI 10.3389/frobt.2024.1341689
Pubmed ID
Authors

Lesia Mochurad

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2024.
All research outputs
#20,583,153
of 25,292,378 outputs
Outputs from Frontiers in Robotics and AI
#1,426
of 1,753 outputs
Outputs of similar age
#107,511
of 156,501 outputs
Outputs of similar age from Frontiers in Robotics and AI
#14
of 37 outputs
Altmetric has tracked 25,292,378 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,753 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 156,501 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.