MMX Rover – DLR autonomous navigation experiment (NavDLR) test data

Parts of our data collection to test the NavDLR autonomous navigation solution.

Welcome to our data set webpage regarding  the field test. Please note that due to legal requirements imposed on us, it is necessary to fill out the registration form below prior to accessing the data set.


The MMX rover will explore the surface of Phobos,
Mars’ bigger moon. It will use its stereo cameras for perceiving the environment, enabling the use of vision based autonomous navigation algorithms. The German Aerospace Center (DLR) is currently developing the corresponding autonomous navigation experiment that will allow the rover to efficiently explore the surface of Phobos, despite limited communication with Earth and long turn-around times for operations.

Our testing is mostly data set-based for which we recreate the environmental conditions on Phobos as closely as possible.
We make the corresponding data set publicly available and provide an overview on its content.

License: CC-BY-NC-SA



Related Work – MADMAX

We used a similar sensor setup (+ additional sensors) in the Sahara Desert of Morocco to record the MADMAX Mars analog data set that is complementary to the data set here.

Get detailed information on MADMAX and have a look into the corresponding Publication.

Corresponding Publication – How to cite us





@inproceedings{WORK IN PROGRESS


List of relevant publications:

  • MADMAX: Mars Analog Data Set from Morocco with identical Sensor Setup:
    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. Free Access:
  • 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:


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