The Málaga Stereo and Laser Urban Data Set

CHANGELOG:

• 2019-01-29: Our downloads server is experiencing technical problems! In the meanwhile, you can download from this alternative location.
• 2018-09-13: Fix typo in “z” coordinate of front stereo camera. It should be 57 mm, not 273 mm. Table 2 in Draft PDF has been updated accordingly.
• 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 files	Size
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

Sensors	Stereo 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

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 (Published here; 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.

3. Downloads: entire dataset

3.1. Plain text

•  malaga-urban-dataset-plain-text.zip (1.4 GB, MD5)
  • It contains raw laser scans, IMU and GPS data, and a list of all image file names.
  • See the included Format readme for the format of text files.

3.2. Images/video

• Raw (unprocessed) images (1024×768, 20fps)
  •  malaga-urban-dataset_raw_images.zip (45GB!!, MD5).
    Contains:
    • Images/*: All  stereo images, in JPEG format, separate left & right camera images.
    • camera_params_raw_1024x768.txt: Calibration parameters.
  • Separate video files for left and right cameras:
    •  malaga-urban-dataset_STEREO_LEFT.avi (3.3 GB)
    •  malaga-urban-dataset_STEREO_RIGHT.avi (3.3 GB)
• Rectified images (1024×768, 20fps)
  •  malaga-urban-dataset_rectified_a=0_1024x768.zip (39GB!!, MD5).
    Contains:
    • Images/*: All stereo files, in JPEG format, separate left & right camera images.
    • camera_params_rectified_a=0_1024x768.txt: Calibration parameters.
    • malaga-urban-dataset_CAM_GPS_rectified_a=0_1024x768.rawlog: Binary log file with only two sensor streams (Camera+GPS).

• Rectified images (800×600, 20fps)
  •  malaga-urban-dataset_rectified_a=0_800x600.zip (27GB!!, MD5). Contains:
    • Images/*: All stereo files, in JPEG format, separate left & right camera images.
    • camera_params_rectified_a=0_800x600.txt: Calibration parameters.
    • malaga-urban-dataset_CAM_GPS_rectified_a=0_800x600.rawlog: Binary log file with only two sensor streams (Camera+GPS).

• Set of image pairs of a checkerboard grabbed with the Bumblebee2 camera, useful to determine the camera rectification parameters:
  •  malaga-urban-dataset_bumblebee2_images_for_calibration.zip (10.1 MB)

3.3. Binary files

•  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”).

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:

•  malaga-urban-dataset_DATASET_PREVIEW.flv (5.2 GB, MD5)

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:

• main.cpp
• CMakeLists.cpp

Example of usage:

6. Additional info

• Extra pictures:

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