argoverse_lidar

This repository allows you to access to the pointcloud of the lidars in argoverse dataset separately.

Upper LiDAR	Lower LiDAR

Installation

It is needed to apply some changes to the argoverse-api:

• With diff.patch file
• Or manually:

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)

Use

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.