== Abstract ==

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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.

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Comment: 7 pages, 8 figure

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== Original document ==

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The different versions of the original document can be found in:

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* [http://arxiv.org/abs/1809.06972 http://arxiv.org/abs/1809.06972]

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* [http://arxiv.org/pdf/1809.06972 http://arxiv.org/pdf/1809.06972]

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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],

: [http://dx.doi.org/10.1109/crv.2019.00023 http://dx.doi.org/10.1109/crv.2019.00023]

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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],

: [https://ieeexplore.ieee.org/document/8781606 https://ieeexplore.ieee.org/document/8781606],

: [https://academic.microsoft.com/#/detail/2965737813 https://academic.microsoft.com/#/detail/2965737813]

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