For driving assistance and autonomous driving systems, it is important to differentiate between dynamic objects such as moving vehicles and static objects such as guard rails. Among all the sensor modalities, RADAR and FMCW LiDAR can provide information regarding the motion state of the raw measurement data. On the other hand, perception pipelines using measurement data from ToF LiDAR typically can only differentiate between dynamic and static states on the object level. In this work, a new algorithm called DONEX was developed to classify the motion state of 3D LiDAR point cloud echoes using an occupancy grid approach. Through algorithmic improvements, e.g. 2D grid approach, it was possible to reduce the runtime. Scenarios, in which the measuring sensor is located in a moving vehicle, were also considered.
@article{arxiv.2212.04265,
title = {DONEX: Real-time occupancy grid based dynamic echo classification for 3D point cloud},
author = {Niklas Stralau and Chengxuan Fu},
journal= {arXiv preprint arXiv:2212.04265},
year = {2022}
}
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Published on "Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS 2022)", 5-7 December 2022, Genova, Italy