Mapless Online Detection of Dynamic Objects in 3D Lidar

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-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.

Comment: 7 pages, 8 figure

Original document

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

• http://arxiv.org/abs/1809.06972

• http://arxiv.org/pdf/1809.06972

• http://xplorestaging.ieee.org/ielx7/8771048/8781597/08781606.pdf?arnumber=8781606,

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

• https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-06972,

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

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