DONEX: Real-time occupancy grid based dynamic echo classification for 3D point cloud
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 measurem...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
08.12.2022
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Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2212.04265 |