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Attention Score in Context
| Title |
Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor
|
|---|---|
| Published in |
Frontiers in Neuroscience, January 2014
|
| DOI | 10.3389/fnins.2013.00275 |
| Pubmed ID | |
| Authors |
Christian Brandli, Thomas A. Mantel, Marco Hutter, Markus A. Höpflinger, Raphael Berner, Roland Siegwart, Tobi Delbruck |
| Abstract |
Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor (DVS) with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor's ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500 Hz were achieved using a line laser of 3 mW at a distance of 45 cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2 mm. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 64 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 34% |
| Researcher | 9 | 14% |
| Student > Master | 7 | 11% |
| Student > Doctoral Student | 4 | 6% |
| Student > Bachelor | 3 | 5% |
| Other | 4 | 6% |
| Unknown | 15 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 26 | 41% |
| Computer Science | 12 | 19% |
| Agricultural and Biological Sciences | 3 | 5% |
| Arts and Humanities | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 14 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 19 December 2023.
All research outputs
#2,616,920
of 25,394,764 outputs
Outputs from Frontiers in Neuroscience
#1,626
of 11,544 outputs
Outputs of similar age
#29,168
of 319,416 outputs
Outputs of similar age from Frontiers in Neuroscience
#11
of 51 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,544 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 85% of its peers.
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 319,416 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.