https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Yoon_et_al_2018aYoon et al 2018a - Revision history2026-04-30T19:59:15ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Yoon_et_al_2018a&diff=199442&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 158047239 to Yoon et al 2018a2021-02-01T23:28:33Z<p>Scipediacontent moved page <a href="/public/Draft_Content_158047239" class="mw-redirect" title="Draft Content 158047239">Draft Content 158047239</a> to <a href="/public/Yoon_et_al_2018a" title="Yoon et al 2018a">Yoon et al 2018a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Yoon_et_al_2018a&diff=199441&oldid=prevScipediacontent: Created page with " == Abstract == This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving..."2021-02-01T23:28:28Z<p>Created page with " == Abstract == This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving-while-scanning operation (motion distortion) of present-day 3D spinning lidar sensors. Our detection method uses a motion-compensated freespace querying algorithm and classifies between dynamic (currently moving) and static (currently stationary) labels at the point level. For a quantitative analysis, we establish a benchmark with motion-distorted lidar data using CARLA, an open-source simulator for autonomous driving research. We also provide a qualitative analysis with real data using a Velodyne HDL-64E in driving scenarios. Compared to existing 3D lidar methods that are model-free, our method is unique because of its setting independence and compensation for pointcloud motion distortion.<br />
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Comment: 7 pages, 8 figure<br />
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== Original document ==<br />
<br />
The different versions of the original document can be found in:<br />
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* [http://arxiv.org/abs/1809.06972 http://arxiv.org/abs/1809.06972]<br />
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* [http://arxiv.org/pdf/1809.06972 http://arxiv.org/pdf/1809.06972]<br />
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* [http://xplorestaging.ieee.org/ielx7/8771048/8781597/08781606.pdf?arnumber=8781606 http://xplorestaging.ieee.org/ielx7/8771048/8781597/08781606.pdf?arnumber=8781606],<br />
: [http://dx.doi.org/10.1109/crv.2019.00023 http://dx.doi.org/10.1109/crv.2019.00023]<br />
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* [https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-06972 https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-06972],<br />
: [https://ieeexplore.ieee.org/document/8781606 https://ieeexplore.ieee.org/document/8781606],<br />
: [https://academic.microsoft.com/#/detail/2965737813 https://academic.microsoft.com/#/detail/2965737813]</div>Scipediacontent