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Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping

Overview of attention for article published in arXiv, March 2022
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Title
Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping
Published in
arXiv, March 2022
DOI 10.3389/frobt.2024.1291426
Authors

Akira Taniguchi, Shuya Ito, Tadahiro Taniguchi

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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 24 June 2024.
All research outputs
#21,159,594
of 25,990,612 outputs
Outputs from arXiv
#534,184
of 960,646 outputs
Outputs of similar age
#338,571
of 450,925 outputs
Outputs of similar age from arXiv
#16,295
of 27,802 outputs
Altmetric has tracked 25,990,612 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 960,646 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 27th percentile – i.e., 27% 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 450,925 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27,802 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.