3D maps and sensor data in the city.

This City3DmapData repository provides the 3D maps and sensor data in an actual city of Japan. This data set also contains 3D LIDAR and IMU data and Pose graphs of the mobile robot used for 3D map construction. You can also use these data sets to compare 3D map construction and LIDAR odometry in your work.

These data are used for evaluation in the article entitled "Semi-Automatic Town-Scale 3D Mapping using Building Information from Publicly Available Maps" in IEEE Access 2022 (URL).

• All dataset (33.4GB) : https://data.airc.aist.go.jp/city-3Dmap-data/route1-6.zip

Data acquistion overview

1. Platform

3D LIDAR Velodyne VLP-16, 360 Horizontal Field of View (FOV) 15Hz.
6-axis IMU Xsens MTi-300 AHRS, 200Hz.
Mobile base Whill Model CR, max velocity 6km/h.
The positional relationship between the sensors is defined by static tf launch in scripts.

2. Route

Route	Distance [km]	Time
1	0.99	11 min 53 sec
2	1.13	19 min 02 sec
3	2.54	29 min 04 sec
4	2.87	33 min 19 sec
5	3.03	39 min 26 sec
6	3.80	47 min 52 sec
Total	14.36	3 hour 0 min 36 sec

Details of the data

Data structure

The dataset is stored in a separate directory for each route written in the paper. These sensor data were collected in Daiba, Koto-Ku, Tokyo, Japan.
The mobile robot was operated manually for moving along each route.

data
└───route1
│   - sensor_data.bag 
│   - 3Dmap.pcd
│   - reference_posegraph.tum 
│   - estimated_posegraph.tum
└───route2
│     ・
│     ・
│     ・
└───route6

1. sensor data

The sensor data is recorded in rosbag format.
Topics:

• 3D LIDAR: /velodyne_points_world_filtered [sensor_msgs/PointCloud2]
• 6-axis IMU: /imu/raw [sensor_msgs/Imu]

2. 3Dmap

The 3D map data is saved as Point Cloud Data (PCD) file format.
The 3D maps are generated by projecting the LIDAR data based on the estimated_posegraph.

3. posegraph

Format tum - TUM RGB-D dataset trajectory format
Every row has eight entries containing timestamp (in seconds), position and orientation (as quaternion), with each value separated by a space:

timestamp x y z q_x q_y q_z q_w

More infos: https://vision.in.tum.de/data/datasets/rgbd-dataset/file_formats

• reference_posegraph
  The reference_posegraph was generated by setting anchor poses manually every 500 frames on the basis of the mobile mapping system's (MMS) 3D maps.
• estimated_posegraph
  The estimated_posegraph was generated by the proposed mapping framework in the paper.

How to visualize the data

Requirements

We used a robot operating system (ROS), point cloud library (PCL) and evo scripts for visualization data and tested by the following version.

• ROS melodic
• pcl 1.8.1
• evo

Sensor data

Start the ROS visualization

scripts/visualize_sensordata.sh

Play the rosbag in another terminal

rosbag play sensor_data.bag --clock

3D map

pcl_viewer 3Dmap.pcd  

Pose graph

evo_traj tum --plot --plot_mode xy --ref reference_posegraph.tum estimated_posegraph.tum

License

This dataset is allowed only for noncommercial usage. Please cite our paper if you use this dataset for your work. The specific terms for use are written in the LICENSE.md.

@ARTICLE{9709335,  
author={Niijima, Shun and Umeyama, Ryusuke and Sasaki, Yoko and Mizoguchi, Hiroshi},  
journal={IEEE Access},   
title={Semi-Automatic Town-Scale 3D Mapping using Building Information from Publicly Available Maps},   
year={2022},  
volume={},  
number={},  
pages={1-1},  
doi={10.1109/ACCESS.2022.3150387}}