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Data Creation

In this section, we demonstrate how to extract expected format data from public datasets (KITTI, Argoverse 2) and also collected by ourselves (rosbag).

Still, I recommend you to download the benchmark data directly from the Zenodo link without reading this section. Back to data download and visualize page. It's only needed for people who want to run more data from themselves.

Create by yourself

If you want to process more data, you can follow the instructions below.

Note

Feel free to skip this section if you only want to use the benchmark data.

Custom Data

For our custom dataset, we normally record the pointcloud with rosbag, and then running some slam methods to get the pose. If you don't have clue to use the slam package, check simple_ndt_slam repo the only dependence you need in the repo is ROS. If you don't have ROS/Ubuntu, you can directly use the docker to run.

Then, directly export rosbag file [which have pose/tf and pointcloud topic] to pcd we want, after your run with simple_ndt_slam check your result rosbag file by rosbag info, here is example output:

➜  bags rosbag info res_semi_indoor_data.bag
path:        res_semi_indoor_data.bag
version:     2.0
duration:    1:47s (107s)
start:       Apr 28 2023 11:11:26.79 (1682673086.79)
end:         Apr 28 2023 11:13:14.35 (1682673194.35)
size:        810.8 MB
messages:    4803
compression: none [961/961 chunks]
types:       nav_msgs/Odometry       [cd5e73d190d741a2f92e81eda573aca7]
             sensor_msgs/PointCloud2 [1158d486dd51d683ce2f1be655c3c181]
             tf2_msgs/TFMessage      [94810edda583a504dfda3829e70d7eec]
topics:      /auto_odom       960 msgs    : nav_msgs/Odometry      
             /repub_points    960 msgs    : sensor_msgs/PointCloud2
             /tf             2883 msgs    : tf2_msgs/TFMessage

Then use the scripts I provided in simple_ndt_slam to extract the pcd data to unified format here.

roscore # since need read rosbag through scripts

./simple_ndt_slam/tools/build/bag2pcd_tf /home/kin/bags/res_semi_indoor_data.bag /home/kin/data/semindoor /repub_points map 1 # 1 for save raw map also since some methods need use it in the framework.

KITTI Dataset

Official data format Download link

Extract Point Cloud Data

extract the semantic-kitti dataset from the raw data, when you download the original SemanticKITTI dataset, you will get a folder like this: 

➜  SemanticKitti tree -L 2
.
├── data_odometry_calib
│   └── dataset
│       └── sequences
├── data_odometry_labels
│   ├── dataset
│   │   └── sequences
│   └── README
├── data_odometry_velodyne
│   └── dataset
│       └── sequences

After downloading the official dataset, Run the script like follows: 

python3 scripts/data/extract_semkitti.py --original_path /home/kin/data/KITTI/SemanticKitti --save_data_folder /home/kin/data/DynamicMap --gt_cloud True --sequence "'00'"

Note!!

  1. SemanticKITTI pose file is not ground truth pose but run SuMa, more discussion and different can be found here in semantic-kitti-api/issues/140. We have extra different odometry pose result in DUFOMap paper, Sec V-C, Table III, based on scripts/py/data/extract_diff_pose.py

  2. You can get the sensor pose in the PCD VIEWPOINT Field, so you don't need pose file etc. If you are using CloudCompare to view, drag all pcd files to windows, you will have the correct whole map view. (NOTE Since we already transform to world frame CloudCompare in 2.11 version will looks correct map but version to 2.12+ will have double effect on VIEWPOINT Field [you can comment the transform line if you don't like that.] )

    Example here:

  3. View the ground truth in CloudCompare, intensity=1 means dynamic which are red points in images:

  4. 2024/03/27 Updated version limit the range because we find the ground truth label is not correct in the far range, so we limit the range to 50m. You can change the range in the script.

Argoverse 2.0 Dataset

I manually labeled dynamic and static in one sequence folder name: 07YOTznatmYypvQYpzviEcU3yGPsyaGg__Spring_2020 , you have to go for website to downloaded this ground truth PCD.

Download

Check this issue: https://github.com/argoverse/av2-api/issues/161

Installing s5cmd

#!/usr/bin/env bash

export INSTALL_DIR=$HOME/.local/bin
export PATH=$PATH:$INSTALL_DIR
export S5CMD_URI=https://github.com/peak/s5cmd/releases/download/v1.4.0/s5cmd_1.4.0_$(uname | sed 's/Darwin/macOS/g')-64bit.tar.gz

mkdir -p $INSTALL_DIR
curl -sL $S5CMD_URI | tar -C $INSTALL_DIR -xvzf - s5cmd

Download the val dataset since train is toooo big for me, totally is 5T for train dataset although no label.

s5cmd --no-sign-request cp 's3://argoai-argoverse/av2/lidar/val/*' /home/kin/bags/av2/val

Extract Point Cloud Data

This time no need cpp file since argoverse have their own api things and we just need to use it. Also I write with save pcd in utils.

Check their python api, github 

pip install av2

Please check the folder path inside the script. 

python3 scripts/extract_argoverse2.py