{
  "$type": "site.standard.document",
  "description": "Embodiments include a method for ground surface segmentation on sparse Light Detection And Ranging (LiDAR) point clouds comprising: reading a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR…",
  "path": "/patents/1290752",
  "publishedAt": "2021-05-20T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G01C21/26",
    "NIO USA, Inc."
  ],
  "textContent": "Embodiments include a method for ground surface segmentation on sparse Light Detection And Ranging (LiDAR) point clouds comprising: reading a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; voxelizing the LiDAR point cloud to produce a three-dimensional representation of each of the one or more objects; constructing a maximum height map from the three-dimensional representation of each of the one or more objects, the maximum height map comprising a two-dimensional mapping of spatial points representing each of the one or more objects; performing minimum filtering on the spatial points of the maximum height map; and classifying each spatial point as a ground point or a non-ground point based on the minimum filtering of each spatial point.",
  "title": "REAL-TIME GROUND SURFACE SEGMENTATION ALGORITHM FOR SPARSE POINT CLOUDS"
}