Convert a rosbag to PCD, online

Frame export

Local tools like bag_to_pcd read one topic and write a folder of timestamped PCD files. That is great for debugging a sensor stream, checking fields, or handing one frame to another tool.

Mapped output

If what you really need is a point cloud map, you still need LiDAR SLAM and, when available, GNSS-backed georeferencing. That is the job LidarFlow is built for.

1. Upstream CLI

If you are already in a ROS environment and only need per-frame exports, use the upstream tool directly.

ros2 run pcl_ros bag_to_pcd --ros-args \
  -p bag_path:=rosbag2_2025_01_01/ \
  -p topic_name:=/pointcloud \
  -p output_directory:=pcds

Good for timestamped frame exports. Not a full mapping pipeline.

2. Python script

If you need custom filtering, field transforms, or one-off post-processing, a small Python script gives you more control.

import rosbag
from sensor_msgs import point_cloud2

with rosbag.Bag("input.bag") as bag:
    for _, msg, stamp in bag.read_messages(topics=["/velodyne_points"]):
        points = list(point_cloud2.read_points(msg, skip_nans=True))
        # write one PCD per frame or feed points into another tool

Flexible, but you still own the rest of the pipeline.

3. LidarFlow

If you want a mapped output instead of a folder of raw frames, upload the recording through the browser and let LidarFlow handle validation, SLAM, georeferencing, and artifact delivery.

• Upload `.mcap` or ROS1 `.bag`
• Validate topics before launch
• Download `output.pcd`, `map.ply`, metadata, and trajectory artifacts

Use the local path when

• You only need frame-by-frame PCDs for debugging one topic.
• You want to inspect timestamps, fields, or per-frame coverage before running SLAM.

Use LidarFlow when

• You need one mapped point cloud and not a directory full of timestamped exports.
• You want one browser surface for run status, QA, and downloads.
• You do not want to maintain ROS, SLAM, and storage glue on every operator laptop.