The DLR Morocco-Acquired Dataset of Mars-Analogue eXploration
from the MOROCCO 2018  Field Test

Welcome to our website corresponding to the MADMAX data set presented in the Journal of Field Robotics, 2021.

The data set consists of 36 different navigation experiments, captured at eight Mars analog sites of widely varying environmental conditions. Its longest trajectory covers 1.5 km and the combined trajectory length is 9.2 km

It contains time-stamped recordings from monochrome stereo cameras, a color camera, omnidirectional cameras in stereo configuration, recordings of an IMU, and a 5 DoF Differential-GNSS ground truth.

We run two state-of-the-art navigation algorithms, ORB-SLAM2 and VINS-mono, on our data to evaluate their accuracy and to provide a baseline, which can be used as a benchmark for accuracy and robustness for other navigation algorithms.

Fast Track

Take me directly to the data.


Collection of short data set snippets to show what you can expect from MADMAX.

Corresponding Publication – How to cite us:

Meyer, L., Smíšek, M., Fontan Villacampa, A., Oliva Maza, L., Medina, D., Schuster, M. J., Steidle, F., Vayugundla, M., Müller, M. G., Rebele, B., Wedler, A., & Triebel, R. (2021). The MADMAX data set for visual‐inertial rover navigation on Mars. Journal of Field Robotics, 121.


author = {Meyer, Lukas and Smíšek, Michal and Fontan Villacampa, Alejandro and Oliva Maza, Laura and Medina, Daniel and Schuster, Martin J. and Steidle, Florian and Vayugundla, Mallikarjuna and Müller, Marcus G. and Rebele, Bernhard and Wedler, Armin and Triebel, Rudolph},
title = {The MADMAX data set for visual-inertial rover navigation on Mars},
journal = {Journal of Field Robotics},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {exploration, extreme environments, navigation, planetary robotics, SLAM},
doi = {},
url = {},

Data-Recording Locations

List of all available Data. Locations A-H from the sites:

  • Rissani 1 (N 31.2983, W 4.3871) –> A,B,C
  • Kess Kess (N 31.3712, W 4.0518) –> D,E
  • Maadid (N 31.5005,  W 4.2202) –> F,G,H

Location Overview

Trajectories of the experiments together with the base station locations are shown in Openstreetmaps:

click to enlarge. you will be redirectes to open streetmap.
copyright openstreet map

Please note that all presented data can be downloaded free of charge to be used in an academic environment.

However, registration is required to access the full list of download links.

Sensor Setup

Sensor Type Name Specifications
Navigation Cameras AlliedVision Mako G-319 4Hz, monochrome 1032 × 772px images, rectified
Color Camera AlliedVision Mako G-319 4Hz, color 2064 × 1544px images, rectified
Camera Lenses RICOH FL-HC0614-2M 6mm, F/1.4
Omnicam Cameras AlliedVision Mako G-319 4-8Hz, monochrome 2064 × 1544px images
Omnicam Lens Entaniya 280 Fisheye 1.07mm, F/2.8, 280 ◦ × 360 ◦ field of view
IMU XSENS MTi-10 IMU MEMS-IMU, 100Hz, three-axis acceleration and three-axis angular velocities
GNSS receiver Piksi Multi GNSS SwiftNav 1Hz, GNSS Data
GNSS antenna SwiftNav GPS500 Frequencies: GPS L1/L2, GLONASS L1/L2 and Bei-
Dou B1/B2/B3

Dataset Structure

All Data clustered by Location X and Run N. The data is provided in human-readable format as text or image files.

Each Run X-N has the following data:

│ │     # Calibration info on original images
│ ├── callab_camera_calibration_stereo.txt
│ ├── callab_camera_calibration_color.txt
│ │
│ │     # Calibration info on rectified images
│ ├── camera_rect_left_info.txt
│ ├── camera_rect_right_info.txt
│ ├── camera_rect_color_info.txt
│ │
│ │     # kinematic information – transformations
│ ├── tf__T_to_B_init_pose.csv
│ ├── tf__IMU_to_camera_left.csv
│ ├── tf__IMU_to_camera_color.csv
│ ├── tf__IMU_to_camera_omni_up.csv
│ ├── tf__IMU_to_camera_omni_down.csv
│ └── tf__IMU_to_B.csv

│ ├── gt_gnss.csv
│ ├── gnss_antenna_base
│ │ ├── gnss_base.obs
│ │ └── gnss_base.nav
│ ├── gnss_antenna_right
│ │ └── …
│ └── gnss_antenna_left
│ └── …

│ ├── orbslam2_nav.csv
│ ├── orbslam2_nav_aligned.csv
│ ├── vins_mono_nav.csv
│ └── vins_mono_nav_aligned.csv

├── imu_data.csv

├── metadata.txt

│ ├── img_rect_left_*.png
│ └── …
│ ├── img_rect_right_*.png
│ └── …
│ ├── img_rect_color_*.png
│ └── …
│ ├── img_rect_depth_*.png
│ └── …
│ ├── img_omni_up_*.png
│ └── …
├── img_omni_down_*.png
└── …


All data are in universally usable formats, such as plain text files or .png images.

We know that especially rosbags are valuable to the community. We already uploaded specific rosbags to our sever. However, to preserve space, we will upload additional rosbags only on request.

If desired, we can provide the original rosbag files. Please contact us separately regarding this request.


The callibration was done with:
DLR CalDe and DLR CalLab – The open-source DLR Camera Calibration Toolbox

The calibration files contain images of the DLR calibration pattern seen by all cameras. The calibration images are found on the download page, together with the calibration results.

Provided files:

  • raw images of the calibration pattern
  • calibration results („callab_camera_calibration_*.txt“)
  • calibration information on the rectified images („camera_rect_*.txt“)

For navigation algorithms with the rectified images, use the camera_rect_*.txt files!

Go to the download page to access the calibration data:


We use the SLAM navigation algorithms VINS-Mono and ORB-SLAM2 for navigation with MADMAX. You are invited to compare your navigation solution against these two algorithms.

We modified the image resolution and the message queue for ORB-SLAM2. See our HOW-TO page for details.

The code: VINS-Mono on
The paper: VINS-Mono: A Robust and Versatile MonocularVisual-Inertial State Estimator

The code: ORB-SLAM2 on
The paper: ORB-SLAM2: an Open-Source SLAM System forMonocular, Stereo and RGB-D Cameras

The resulting navigation Data can be downloaded in the download section. The trajectories of VINS/ORBSLAM are shown for each individual experiment under „Location Details“.

The performance of the algorithms is shown here:

Percentage of trajectory completed.

Percentage of trajectory completed. Only results with 75%+ completion rate are considered for subsequent analysis.


Navigation Performance: RPE of the navigation algorithms for each experiment.

Navigation Performance: RPE of the navigation algorithms for each experiment.

Navigation Performance: ATE of the navigation algorithms for each experiment.

Navigation Performance: ATE of the navigation algorithms for each experiment.


GNSS Ground Truth:

We computed the ground truth using our own algorithms. However, you can use the provided data of the left, right, and base antenna to compute your own ground truth. The provided raw data can be processed using RTKLIB:
RTKLIB: An Open Source Program Package for GNSS Positioning

Our GNSS setup is shown in the figure below (left). We use two antennas with a baseline of 1.285m to calculate the combined ground truth position and orientation of the Body frame B of SUPER, relative to the Topocentric frame T.

Our algorithmic approach to obtain the ground truth is shown below (right). Everything is described in detail in our paper.

Known Issues

Frame Drops

As discussed in the publication, errors with the Ethernet connection of the cameras caused drops of frames.
How much each experiment was affected is shown below.
Interestingly, our evaluation with ORBSLAM2 and VINS-mono did not establish any correlation between frame-drops and navigation accuracy / robustness.

Percentage of frame drops compared to overal number of frames.

Percentage of frame drops compared to overall number of frames.

Camera decalibration with the G-runs

During the G runs, the stereo camera intrinsics got decalibrated due to an mechanical impact. This results in slightly inaccurate depth images. SLAM based algorithms can usually handle this and provide correct navigation results.



Length and character of trajectories

Location   Character ID Length [m] Type
A   Flat area with stones, rock ridge at the end of the area. Low sun illumination. A0 133 homing
A1 138 homing
A2 134 homing
A3 219 zig‐zag + homing
A4 250 mapping
A5 200 mapping
A6 258 exploration
B   Flat area with sand and pebbles and few big rocks, cliffs visible in the background. B0 1511 long range nav
B1 193 homing
B2 195 homing
B3 195 homing
B4 286 zig‐zag + homing
B5 301 mapping
B6 293 exploration
B7 312 exploration
C   Small flat and square area, half sandy and half stony. C0 341 zig‐zag
C1 321 zig‐zag
C2 378 zig‐zag
D   Flat area with small stones and pebbles, hill formation in the background D0 141 circular homing
D1 155 circular homing
D2 134 circular homing
D3 493 long range nav
D4 422 exploration run
E   Rough terrain inside a valley and big stones within the traversed path. E0 223 exploration
E1 309 exploration
E2 374 exploration
F   Flat area with small pebbles, rough terrain at the end of the area. F0 121 homing
F1 128 homing
F2 121 homing
F3 172 zig‐zag + homing
F4 167 mapping
F5 141 mapping
G   Navigation around big composite rock boulders with sandy surface in between. G0* 125 exploration
G1* 115 exploration
G2* 154 exploration
H   Desert sand dunes. H0 90 exploration

* marks trajectories, where a decalibration of the stereo camera extrinsics occurred.

Handheld field testing is representative for Rover navigation

As described in our paper (section 6.3), we conclude representativeness of our data set for planetary rover navigation, as the navigation algorithms have the same accuracy compared to similar Rover-based navigation data sets.


This work was funded by the DLR project MOdulares Robotisches EXplorationssystem (MOREX).

This activity has been conducted jointly with the two European Commission Horizon 2020 Projects InFuse and Facilitators. They received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 730068 and 730014.


As mentioned above, if you use the data from the data set in your own work, please cite us in the following way:

Meyer, L., Smíšek, M., Fontan Villacampa, A., Oliva Maza, L., Medina, D., Schuster, M. J., Steidle, F., Vayugundla, M., Müller, M. G., Rebele, B., Wedler, A., & Triebel, R. (2021). The MADMAX data set for visual‐inertial rover navigation on Mars. Journal of Field Robotics, 121.

List of relevant publications:

  • Detailed system description of the Lightweight Rover Unit LRU (the technical reference of SUPER):
    Schuster, Martin J. et al. (2017) Towards Autonomous Planetary Exploration: The Lightweight Rover Unit (LRU), its Success in the SpaceBotCamp Challenge, and Beyond. Journal of Intelligent & Robotic Systems. Springer. doi: 10.1007/s10846-017-0680-9.
    Free Access:
  • Etna Long Range Navigation Test – Moon-analogue navigation data set with identical sensor setup as SUPER:
    Vayugundla, Mallikarjuna et al. (2018) Datasets of Long Range Navigation Experiments in a Moon Analogue Environment on Mount Etna. In: 50th International Symposium on Robotics, pp. 1-7. ISR 2018; 50th International Symposium on Robotics, 20-21 June 2018, Munich, Germany
    Free Access:
  • Other publications related to the overall Morocco Field Test campaign:
    • Post, Mark et al. (2018) InFuse Data Fusion Methodology for Space Robotics, Awareness and Machine Learning. In: 69th International Astronautical Congress, Oct 2018, Bremen, Germany;
    • Lacroix, Simon et al. (2019). The Erfoud dataset: A comprehensive multi‐camera and Lidar data collection for planetary exploration. Proceedings of the Symposium on Advanced Space Technologies in Robotics and Automation-

Related Work

Etna Long Range Navigation Test – Moon-analogue navigation data set with identical sensor setup as SUPER

Data Set Website:


To gain access to the full list of sets please fill in. You will receive an email with the details.