This repository allows you to access to the pointcloud of the lidars in argoverse dataset separately.
Upper LiDAR | Lower LiDAR |
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It is needed to apply some changes to the argoverse-api:
In /argoverse/data_loading/argoverse_tracking_loader.py:
Modify the import of load_ply:
- from argoverse.utils.ply_loader import load_ply
+ from argoverse.utils.ply_loader import load_ply, load_ply_ring
Add this function:
def get_lidar_ring(self, idx: int, log_id: Optional[str] = None, load: bool = True) -> Union[str, np.ndarray]:
"""Get lidar corresponding to frame index idx (in lidar frame).
Args:
idx: Lidar frame index
log_id: ID of log to search (default: current log)
load: whether to load up the data, will return path to the lidar file if set to false
Returns:
Either path to lidar at a specific index, or point cloud data if load is set to True
"""
assert self.lidar_timestamp_list is not None
assert self._lidar_timestamp_list is not None
assert self.lidar_list is not None
assert self._lidar_list is not None
if log_id is None:
log_id = self.current_log
assert idx < len(self._lidar_timestamp_list[log_id])
if load:
return load_ply_ring(self._lidar_list[log_id][idx])
return self._lidar_list[log_id][idx]
In /argoverse/utils/ply_loader.py:
Add this function:
def load_ply_ring(ply_fpath: _PathLike) -> np.ndarray:
"""Load a point cloud file from a filepath.
Args:
ply_fpath: Path to a PLY file
Returns:
arr: Array of shape (N, 3)5
"""
data = pyntcloud.PyntCloud.from_file(os.fspath(ply_fpath))
x = np.array(data.points.x)[:, np.newaxis]
y = np.array(data.points.y)[:, np.newaxis]
z = np.array(data.points.z)[:, np.newaxis]
i = np.array(data.points.intensity)[:, np.newaxis]
ring = np.array(data.points.laser_number)[:, np.newaxis]
return np.concatenate((x, y, z, i, ring), axis=1)
The script get_velodyne.py reads the lidar pointclouds from the argoverse dataset. You can configure which log to reproduce by changing this variables:
root_dir = 'ARGOVERSE_ROOT/argoverse-api/argoverse-tracking/sample/'
log_id = 'c6911883-1843-3727-8eaa-41dc8cda8993'
The script uses ROS and publishes both pointclouds in different messages. The velo2car.launch file publishes the tf transformations between the lidars and the vehicle coordinate system.