The Málaga Stereo and Laser Urban Data Set

CHANGELOG:

  • 2017-05-10: Add calibration and data sheet of the MTi iMU.
  • 2014-05-15: All images have been rectified again such that both stereo image centers coincide. This simplifies the number of parameters needed to perform stereo SLAM or visual odometry. Calibration files of rectified images have been updated.
  • 2013-10-09: First version online.
Download dataset filesSize
malaga-urban-dataset-extract-01.zip 883 MB
malaga-urban-dataset-extract-02.zip 2.0 GB
malaga-urban-dataset-extract-03.zip 796 MB
malaga-urban-dataset-extract-04.zip 653 MB
malaga-urban-dataset-extract-05.zip 4.4 GB
malaga-urban-dataset-extract-06.zip 4.4 GB
malaga-urban-dataset-extract-07.zip 2.2 GB
malaga-urban-dataset-extract-08.zip 10 GB
malaga-urban-dataset-extract-09.zip 968 MB
malaga-urban-dataset-extract-10.zip 17 GB
malaga-urban-dataset-extract-11.zip 2.3 GB
malaga-urban-dataset-extract-12.zip 8.5 GB
malaga-urban-dataset-extract-13.zip 33 GB
malaga-urban-dataset-extract-14.zip 1.9 GB
malaga-urban-dataset-extract-15.zip 1.3 GB
SensorsStereo camera, IMU, GPS, 2xSICK LMS, 3xHOKUYO
Recorded at:Málaga (Spain)
Additional info:
This dataset was gathered entirely in urban scenarios with a car equipped with several sensors, including one stereo camera (Bumblebee2) and five laser scanners. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at high rate (20fps) during a 36.8km trajectory, turning the dataset into a suitable benchmark for a variety of computer vision techniques. Both plain text and binary files are provided, as well as open source tools for working with the binary versions.

1. Summary

The car after installing the sensors.

The car after installing the sensors.

This dataset was gathered entirely in urban scenarios with a car equipped with several sensors, including one stereo camera (Bumblebee2) and five laser scanners. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at high rate (20fps) during a 36.8km trajectory, turning the dataset into a suitable benchmark for a variety of computer vision techniques. Both plain text and binary files are provided, as well as open source tools for working with the binary versions.

Citation:

J.L. Blanco-Claraco, F.A. Moreno-Dueñas, J. Gonzalez-Jimenez. The Málaga Urban Dataset: High-rate Stereo and Lidars in a realistic urban scenario“, The International Journal of Robotics Research (IJRR), Feb 2014, vol. 33, no. 2, 207-214. (Bibtex) DOI: 10.1177/0278364913507326 (OnlineFirst; Draft PDF ).

2. Downloads: selected extracts

For the convenience of usage we provide separate downloads for a number of selected sections of the dataset.
It should be much easier and convenient to work with these sets instead of the entire dataset, available below.

All packages include raw and rectified stereo images, all data in plain text files and in binary (.rawlog) format.

Description
and download
Path overview Video summary (Click to play)
Extract #01:
Straight path in the faculty parking.

Extract #02:
Through an under-construction road.

Extract #03:
Roundabout 3/4 turn.

Extract #04:
Roundabout with traffic.

Extract #05:
Avenue loop closure (~1.7 km).

Extract #06:
Block loop closure (~1.2 km).

Extract #07:
Short avenue loop closure (~0.7 km).

Extract #08:
Long loop closure (~4.5 km).

Extract #09:
Campus boulevard (with traffic).

Extract #10:
Multiple loop closures.

Extract #11:
High-way incorporation (with traffic).

Extract #12:
Long avenue with traffic (~3.7 km).

Extract #13:
Downtown (traffic,pedestrians).

Extract #14:
Direct sun example.

Extract #15:
Direct sun example.

3. Downloads: entire dataset

3.1. Plain text

3.2. Images/video

3.3. Binary files

  • download malaga-urban-dataset-binary.zip (1.3 GB, MD5) – Contents of this file:
    • malaga-urban-dataset_all-sensors.rawlog: The main binary log with all sensors for the entire dataset.
    • malaga-urban-dataset_CAM+GPS.rawlog: A filtered version of the one above, where the only sensor streams are that of the stereo camera and the
         GPS receiver.
    • malaga-urban-dataset_CAM+GPS.kml: A Google Earth file with a representation of the dataset path (as generated with the tool “rawlog-edit”).
All .rawlog files can be opened with the program “RawLogViewer“, part of MRPT. In Ubuntu, install the package “mrpt-apps“.
Note that raw (non undistorted) images are NOT included in these rawlogs. They are available for download in a link above. If you use MRPT apps or C++ classes to parse the .rawlog files, please create a directory named “Images” and put all images there for the software to find them.

4. Overview of the entire dataset

4.1. Path in Google Maps

The following map has been obtained from the GPS sensor onboard the vehicle during the whole dataset run.
You can zoom in to see the path in more detail.



4.2. Video index

This composition shows:

  • Top-left: raw images from stereo (left) camera.
  • Bottom-left: The position from GPS, overlaid to a map of the city.
  • Right: A 3D reconstruction of the environment by very simple interpolation of GPS points. Only two lasers (out of five) are used in this view.
  • You can also see the exact timestamp for each instant, which is useful to directly skip to an interesting part for your application.

The video is also available for download for offline usage:

5. Parsing the logs (C++ code)

Apart from plain text files, binary .rawlog files are an efficient and convenient way of parsing vision and robotic datasets
from C++ code. The following code is provided as a starting point from which to write your own programs:

Example of usage:


./parse_dataset_example malaga-urban-dataset-extract-01/malaga-urban-dataset-extract-01_rectified_800x600.rawlog

6. Additional info

  • Extra pictures:
Sensors before mounting on the car.

Sensors before mounting on the car.

The car after installing the sensors.

The car after installing the sensors.

  • Analysis of the (complete) rawlog file using “rawlog-edit --info“:

$ rawlog-edit --info -i malaga-urban-dataset_all-sensors.rawlog
[rawlog-edit] Operation to perform: info
[rawlog-edit] Opening 'malaga-urban-dataset_all-sensors.rawlog'...
[rawlog-edit] Open OK.
[rawlog-edit] Found external storage directory: Images
Progress: 2146534 objects --- Pos:    4.76 GB/>   1.41 GB
Time to parse file (sec)          : 38.7593
Physical file size                : 1.41 GB
Uncompressed file size            : 4.79 GB
Compression ratio                 : 29.36%
Overall number of objects         : 2153717
Actions/SensoryFrame format       : No
Observations format               : Yes
All sensor labels                 : CAMERA1, GPS_DELUO, HOKUYO1, HOKUYO2, HOKUYO3, LASER1, LASER2, XSensMTi
Sensor (Label/Occurs/Rate/Durat.) :         CAMERA1 / 113082 /19.998 /5654.574
Sensor (Label/Occurs/Rate/Durat.) :       GPS_DELUO /  11244 /1.989 /5653.000
Sensor (Label/Occurs/Rate/Durat.) :         HOKUYO1 / 225416 /39.864 /5654.617
Sensor (Label/Occurs/Rate/Durat.) :         HOKUYO2 / 225631 /39.902 /5654.624
Sensor (Label/Occurs/Rate/Durat.) :         HOKUYO3 / 225510 /39.880 /5654.621
Sensor (Label/Occurs/Rate/Durat.) :          LASER1 / 398531 /74.974 /5315.578
Sensor (Label/Occurs/Rate/Durat.) :          LASER2 / 404487 /73.568 /5498.109
Sensor (Label/Occurs/Rate/Durat.) :        XSensMTi / 549816 /100.000 /5498.150