Skip to main content
Log in

Active Asteroid-SLAM

Active Graph SLAM with Landing Site Discovery in a Deep Space Proximity Operations Scenario

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript


In this paper, we propose an active real-time capable 3D graph based simultaneous localization and mapping (Graph SLAM) approach, which actively estimates the state of an autonomous spacecraft relative to a simultaneously established map estimate. The graph is constructed in a tightly-coupled fashion, where an Extended Kalman Filter estimates the relative offset between two of its vertices. An additional relative measurement is derived by matching point clouds obtained by a light detection and ranging (LiDAR) system. In order to yield a significant speed-up, scan matching is implemented on the GPU. To reduce the uncertainty of either the state or the map estimate, we present an approach to actively control the system resting on an extended representation of uncertainty in the map. Furthermore, it adapts its behavior depending on the current uncertainty distribution in order to find a dynamic trade-off between exploitation (improve localization performance) and exploration (improve knowledge about the environment). Finally, we present a post-processing approach to discover landing sites in the map estimate without prior knowledge. The evaluation is conducted in a numerical simulation, where the spacecraft explores the real 3D model of Itokawa in its actual dynamic environment. Within that simulation, we use a shader-based GPU implementation for simulating LiDAR measurements. We evaluate the performance of the active SLAM approach and demonstrate that the use of the adaptive approach improves navigation and exploration performance at the same time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  1. Albus, J.S., Lumia, R., McCain, H.: Hierarchical Control of Intelligent Machines Applied to Space Station Telerobots. IEEE Trans. Aerosp. Electron. Syst. 24.5, 535–541 (1988)

    Article  Google Scholar 

  2. Amzajerdian, F., et al.: Utilization of 3D Imaging Flash LiDAR Technology for Autonomous Safe Landing on Planetary Bodies. In: Quantum Sensing and Nanophotonic Devices VII. vol. 7608, pp. 760828. International Society for Optics and Photonics (2010)

  3. Andert, F., Ammann, N., Maass, B.: Lidar-Aided Camera Feature Tracking and Visual SLAM for Spacecraft Low-Orbit Navigation and Planetary Landing. In: Advances in Aerospace Guidance, Navigation and Control. Springer, pp. 605–623 (2015)

  4. Ang, M.H., Tourassis, V.D.: Singularities of Euler and Roll-Pitchyaw Representations. IEEE Trans. Aerosp. Electron. Syst. 3, 317–324 (1987)

    Article  Google Scholar 

  5. Badescu, V.: Asteroids: Prospective energy and material resources. Springer Science & Business Media (2013)

  6. Berry, K., et al.: OSIRIS-REx Touch-And-Go (TAG) Mission Design and Analysis. In: 36Th Annual AAS Guidance and Control Conference. American Astronautical Society; Rocky Mountain Section, Breckenridge (2013)

  7. Biber, P., Fleck, S., Straßr, W.: A Probabilistic Framework for Robust and Accurate Matching of Point Clouds. In: Joint Pattern Recognition Symposium, pp. 480–487. Springer (2004)

  8. Biber, P., Straßr, W.: The Normal Distributions Transform A New Approach to Laser Scan Matching. IROS 3, 2743–2748 (2003)

    Google Scholar 

  9. Anthony, G.A., et al.: Brown Gaia Data Release 2. Summary of the Contents and Survey Properties. In: arXiv:1804.09365 (2018)

  10. Budnik, F., Morley, T., Mackenzie, R.: ESOC’S System for Interplanetary Orbit Determination Implementation and Operational Experience. In: 18Th International Symposium on Space Flight Dynamics. vol. 548, pp. 387–392 (2004)

  11. Büskens, C., Wassel, D.: The ESA Nlp Solver WORHP. In: Modeling and Optimization in Space Engineering, pp. 85–110. Springer (2012)

  12. Carlone, L., et al.: Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence. J. Intell. Robot. Syst. 75.2, 291–311 (2014)

    Article  Google Scholar 

  13. Carrillo, H., Reid, I., Castellanos, J.A.: On the Comparison of Uncertainty Criteria for Active SLAM. In: 2012 IEEE International Conference On Robotics and Automation (ICRA), pp. 2080–2087. IEEE (2012)

  14. Christian, J.A., Cryan, S.: A Survey of LIDAR Technology and Its Use in Spacecraft Relative Navigation. In: AIAA Guidance, Navigation, and Control (GNC) Conference, pp. 4641 (2013)

  15. Claraco, J.L.B.: Development of scientific applications with the mobile robot programming toolkit. In: (2008)

  16. Clemens, J.: Multi-Robot In-Ice Localization Using Graph Optimization. In: 2017 IEEE International Conference On Autonomous Robot Systems and Competitions (ICARSC), pp. 36–42. . IEEE (2017)

  17. Clemens, J., Reineking, T., Kluth, T.: An Evidential Approach to SLAM, path planning, and active exploration. In: International Journal of Approximate Reasoning 73, pp. 1–26 (2016)

  18. Clemens, J., Schill, K.: Extended Kalman Filter with Manifold State Representation for Navigating a Maneuverable Melting Probe. In: 2016 19Th International Conference On Information Fusion (FUSION), pp. 1789–1796. IEEE (2016)

  19. Cocaud, C., Kubota, T.: Autonomous navigation near asteroids based on visual SLAM. In: Proceedings of the 23rd International Symposium on Space Flight Dynamics, Pasadena, California (2012)

  20. Cocaud, C., Kubota, T.: SURF-Based SLAM Scheme Using Octree Occupancy Grid for Autonomous Landing on Asteroids. In: International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), pp. 275–282 (2010)

  21. Curkendall, D.W., Border, J.S.: Delta-DOR The One-Nanoradian Navigation Measurement System of the Deep Space network-History, Architecture, and Componentry. In: The Interplanetary Network Progress Report 42, pp. 193 (2013)

  22. Davis, T.A.: Direct methods for sparse linear systems. vol. 2 SIAM (2006)

  23. Dellaert, F., Kaess, M.: Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing. Int. J. Robot. Res. 25.12, 1181–1203 (2006)

    Article  Google Scholar 

  24. Dietrich, A., McMahon, J.W.: Orbit Determination Using Flash Lidar around Small Bodies. J. Guid. Control Dyn. 40.3, 650–665 (2016)

    Google Scholar 

  25. Dietrich, A.B., McMahon, J.W.: Robust Orbit Determination with Flash Lidar around Small Bodies. In: Journal of Guidance, Control, and Dynamics, pp. 1–22 (2018)

  26. Dissly, R., et al.: Flash Lidars for Planetary Missions. In: Workshop on Instrumentation for Planetary Missions (2012)

  27. D’Souza, C.: Autonomous Deep-Space Optical Navigation Project. Technical Report. NASA Johnson Space Center, Houston (2014)

  28. Feder, H.J.S., Leonard, J.J., Smith, C.M.: Adaptive Mobile Robot Navigation and Mapping. Int. J. Robot. Res. 18.7, 650–668 (1999)

    Article  Google Scholar 

  29. Frese, U., Larsson, P., Duckett, T.: A Multilevel Relaxation Algorithm for Simultaneous Localization and Mapping. IEEE Trans. Robot. 21.2, 196–207 (2005)

    Article  Google Scholar 

  30. Golfarelli, M., Maio, D., Rizzi, S.: Elastic Correction of Deadreckoning Errors in Map Building. In: 1998. Proceedings., 1998 IEEE/RSJ International Conference On Intelligent Robots and Systems. Vol. 2, pp. 905–911. IEEE (1998)

  31. Peytavý, G.G., et al.: Autonomous orbit navigation for a mission to the asteroid main belt. In: Proceedings of the 66th International Astronautical Congress, International Astronautical Federation, Jerusalem (2015)

  32. Grisetti, G., Stachniss, C., Burgard, W.: Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters. IEEE Trans. Robot. 23.1, 34–46 (2007). ISSN: 1552-3098.

    Article  Google Scholar 

  33. Grisetti, G., et al.: A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps Using Gradient Descent. In: Robotics: Science and Systems (RSS) (2007)

  34. Grisetti, G., et al.: A Tutorial on Graph-Based SLAM. IEEE Intell. Transp. Syst. Mag. 2.4, 31–43 (2010)

    Article  Google Scholar 

  35. Hahnel, D., et al.: An efficient FastSLAM algorithm for generating maps of largescale cyclic environments from raw laser range measurements. In: 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 1, pp. 206–211. IEEE (2003)

  36. Hertzberg, C., et al.: Integrating Generic Sensor Fusion Algorithms with Sound State Representations through Encapsulation of Manifolds. Inf. Fusion 14.1, 57–77 (2013)

    Article  Google Scholar 

  37. Ho, T.-M., et al.: MASCOT – the Mobile Asteroid Surface Scout Onboard the HAYABUSA2 Mission. Space Sci. Rev. 208.1-4, 339–374 (2017)

    Article  Google Scholar 

  38. Hornung, A., et al.: Octomap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Auton. Robot. 34.3, 189–206 (2013)

    Article  Google Scholar 

  39. Andrew, E., et al.: Johnson a General Approach to Terrain Relative Navigation for Planetary Landing. In: AIAA Aerospace@ Infotech Conf., Rohnert Park (2007)

  40. Kam, H.R., et al.: Rviz: a Toolkit for Real Domain Data Visualization. Telecomm. Syst. 60.2, 337–345 (2015)

    Article  Google Scholar 

  41. Kato, S., et al.: An Open Approach to Autonomous Vehicles. IEEE Micro 35.6, 60–68 (2015). ISSN: 0272-1732.

    Article  Google Scholar 

  42. Kato, S., et al.: Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems. In: Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems. ICCPS ’18, pp. 287–296. IEEE Press, Piscataway. ISBN: 978-1-5386-5301-2. (2018)

  43. Kiefer, J.: General Equivalence Theory for Optimum Designs (Approximate Theory). In: The Annals of Statistics, pp. 849–879 (1974)

  44. Klir, J.: Uncertainty and information: foundations of generalized information theory. Wiley, New York (2005)

  45. Klosko, S.M., AWagner, C.: Spherical harmonic representation of the gravity field from dynamic satellite data. Planet. Space Sci. 30.1, 5–28 (1982). ISSN: 0032-0633.

    Article  Google Scholar 

  46. Kohlbrecher, S., et al.: Hector open source modules for autonomous mapping and navigation with rescue robots. In: Robot SoccerWorld Cup, pp. 624–631. Springer (2013)

  47. Kontitsis, M., Tsiotras, P., Theodorou, E.: An informationtheoretic active localization approach during relative circumnavigation in orbit. In: AIAA Guidance, Navigation, and Control Conference, pp. 0872 (2016)

  48. Kraja, F., Acher, G., Bode, A.: Designing Spacecraft High Performance Computing Architectures. In: Advanced Computing, pp. 137–156. Springer (2013)

  49. Kümmerle, R., et al.: g2o: A general framework for graph optimization. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3607–3613. IEEE (2011)

  50. Landis, R., Johnson, L.: Advances in Planetary Defense in the United States. In: Acta Astronautica (2018)

  51. Laux, T.: ASC’s 3D Flash LIDARTM Camera: The Science behind ASC’s 3D Depth Imaging Video Camera. In: SMPTE International Conference on Stereoscopic 3D for Media and Entertainment, pp. 1–10. (2010)

  52. Magnusson, M.: The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection. PhD Thesis, Örebro Universitet (2009)

  53. Martinez-Cantin, R., et al.: A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Auton. Robot. 27.2, 93–103 (2009). ISSN: 1573–7527.

    Article  Google Scholar 

  54. Montemerlo, M., Roy, N., Thrun, S.: Perspectives on Standardization in Mobile Robot Programming The Carnegie Mellon Navigation (CARMEN) Toolkit. In: 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference On Intelligent Robots and Systems. vol. 3, pp. 2436–2441. IEEE (2003)

  55. Montemerlo, M., et al.: FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges. In: IJCAI, pp. 1151–1156 (2003)

  56. Montemerlo, M., et al.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. In: Aaai/Iaai, pp. 593–598 (2002)

  57. Anastasios, I., et al.: Mourikis Vision-aided Inertial Navigation for Spacecraft Entry, Descent, and Landing. IEEE Trans. Robot. 25.2, 264–280 (2009)

    Google Scholar 

  58. Murray, R.M., Li, Z., Shankar Sastry, S.: A mathematical introduction to robotic manipulation. CRC Press, Boca Raton (1994)

  59. Nakath, D., Clemens, J., Rachuy, C.: Rigid Body Attitude Control Based on a Manifold Representation of Direction Cosine Matrices. In: 13Th European Workshop on Advanced Control and Diagnosis (ACD), vol. 783. Journal of Physics: Conference Series. (2016)

  60. Nakath, D., Clemens, J., Schill, K.: Multi-Sensor Fusion and Active Perception for Autonomous Deep Space Navigation. In: 2018 21St International Conference on Information Fusion (FUSION). FUSION 2018, Cambridge (2018)

  61. Nakath, D., et al.: Optimal Rotation Sequences for Active Perception. In: Braun, J.J. (ed.) Proceedings of SPIE: Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, vol. 9872, pp 987204–1–987204–13. SPIE Press (2016).

  62. Olson, E., Leonard, J., Teller, S.: Fast iterative alignment of pose graphs with poor initial estimates. In: 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on Robotics and Automation, pp. 2262–2269. IEEE (2006)

  63. Olson, E.B.: Real-Time Correlative Scan Matching. In: Ann Arbor 1001, pp. 48109 (2009)

  64. Olson, E.B.: Robust and Efficient Robotic Mapping. AAI0821013. PhD Thesis. Cambridge (2008)

  65. Michael, A.C., et al.: Perryman the HIPPARCOS Catalogue. Astron. Astrophys. 323, L49–L52 (1997)

    Google Scholar 

  66. Pesce, V., Agha-mohammadi, A.-A., Lavagna, M.: Autonomous navigation & mapping of small bodies. In: 2018 IEEE Aerospace Conference, pp. 1–10. (2018)

  67. Probst, A., Förstner, R.: Spacecraft Design of a Multiple Asteroid Orbiter with Re-Docking Lander. In: Advances in Space Research (2017)

  68. Probst, A., et al.: Kanaria: Identifying the Challenges for cognitive autonomous navigation and guidance for missions to small planetary bodies. In: Proceedings of the 66th International Astronautical Congress. International Astronautical Federation, Jerusalem (2015)

  69. Probst, A., et al.: Mission Concept Selection for an Asteroid Mining Mission, vol. 88.3 (2016)

  70. Quigley, M., et al.: ROS: an Open-Source Robot Operating System. In: ICRA Workshop on Open Source Software. Vol. 3.3.2, pp. 5. Kobe, Japan (2009)

  71. Reineking, T.: Belief functions: Theory and algorithms. PhD thesis, University of Bremen (2014)

  72. Reineking, T., Clemens, J.: Dimensions of Uncertainty in Evidential Grid Maps. In: International Conference on Spatial Cognition, pp. 283–298. Springer (2014)

  73. Reineking, T., Clemens, J.: Evidential FastSLAMfor Grid Mapping. In: 2013 16Th International Conference On Information Fusion (FUSION), pp. 789–796. IEEE (2013)

  74. Risler, M.: Behavior control for single and multiple autonomous agents based on hierarchical finite state machines. Phd thesis tuprints (2010)

  75. Randi, J., et al.: Rost openGL shading language. Pearson Education, London (2009)

  76. Roy, N., Thrun, S.: Coastal Navigation with Mobile Robots. In: Advances in Neural Information Processing Systems, pp. 1043–1049 (2000)

  77. Rusu, R.B., Cousins, S.: 3D is Here Point Cloud Library (Pcl). In: 2011 IEEE International Conference On Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)

  78. Rusu, R.B., Cousins, S.: 3D is Here Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA). Shanghai (2011)

  79. Schattel, A.: Dynamic Modeling and Implementation of Trajectory Optimization, Sensitivity Analysis, and Optimal Control for Autonomous Deep Space Navigation. PhD thesis. Center for Industrial Mathematics (ZeTeM), University of Bremen. (2018)

  80. Schattel, A., Echim, M., Büskens, C.: Low Thrust Trajectory Optimization for Autonomous Asteroid Rendezvous Missions. In: 6Th International Conference on Astrodynamics Tools and Techniques (ICATT), Darmstadt, Germany (2016)

  81. Schattel, A., et al.: Optimization and Sensitivity Analysis of Trajectories for Autonomous Small Celestial Body Operations. In: Progress in Industrial Mathematics at ECMI 2016. Ed. by Peregrina Quintela Others. vol. 26 ECMI 2016. Springer (2017)

  82. Scheeres, D., et al.: The Actual Dynamical Environment about Itokawa. In: AIAA / AAS Astrodynamics Specialist Conference and Exhibit, pp. 6661 (2006)

  83. Shafer, G.: A mathematical theory of evidence, vol. 42. Princeton University Press, Princeton (1976)

  84. Shen, Y.-F., et al.: A Vision-Based Automatic Safe Landing-Site Detection System. IEEE Trans. Aerosp. Electron. Syst. 49.1, 294–311 (2013)

    Article  Google Scholar 

  85. Sim, R., Roy, N.: Global a-optimal robot exploration in slam. In: 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 661–666. IEEE (2005)

  86. Smets, P., Kennes, R.: The Transferable Belief Model. Artif. Intell. 66.2, 191–234 (1994)

    Article  MathSciNet  Google Scholar 

  87. Smith, R., Self, M., Cheeseman, P.: Estimating Uncertain Spatial Relationships in Robotics. In: Autonomous Robot Vehicles, pp. 167–193. Springer (1990)

  88. Stachniss, C., Grisetti, G., Burgard, W.: Information Gain-Based Exploration Using Rao-Blackwellized Particle Filters. Robot. Sci. Syst. 2, 65–72 (2005)

    Google Scholar 

  89. van Susante, P., Gertsch, L.: Minerals from Space: Terrestrial and Extraterrestrial Perspectives. In: Conference: Earth and Space 2018 At. IEEE, Cleveland (2018)

  90. Thrun, S., Burgard, W., Fox, D.: A Real-Time Algorithm for Mobile Robot Mapping with Applications to Multi-Robot and 3D Mapping. In: 2000. Proceedings. ICRA’00. IEEE International Conference On Robotics and Automation. vol. 1, pp. 321–328. IEEE (2000)

  91. Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. MIT Press, Cambridge (2005)

  92. Tsuda, Y., et al.: System Design of the Hayabusa 2-Asteroid Sample Return Mission to 1999 JU3. Acta Astronaut. 91, 356–362 (2013)

    Article  Google Scholar 

  93. DWarner, B., Harris, A.W., Pravec, P.: The Asteroid Lightcurve Database. Icarus 202.1, 134–146 (2009)

    Article  Google Scholar 

  94. Wertz, J.R., Everett, D.F., Puschell, J.J.: Space mission engineering: the new SMAD. Microcosm Press, Cleveland (2011)

  95. OWoods, J., Christian, J.A.: Glidar: an OpenGL-Based, Real-Time, and Open Source 3D Sensor Simulator for Testing Computer Vision Algorithms, vol. 2.1 (2016)

  96. Yoshikawa, M., Fujiwara, A., Kawaguchi, J.: Hayabusa and its adventure around the tiny asteroid Itokawa. Proceedings of the International Astronomical Union 2.14, 323–324 (2006).

    Article  Google Scholar 

  97. Zhou, K., Doyle, J.C., Glover, K., et al.: Robust and optimal control, vol. 40. Prentice Hall, New Jersey (1996)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to David Nakath.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the German Aerospace Center (DLR) with financial means of the German Federal Ministry for Economic Affairs and Energy (BMWi), project “KaNaRiA” (grant No. 50 NA 1318) and project “EnEx-CAUSE” (grant No. 50 NA 1505). In addition, we gratefully acknowledge the support of NVIDIA Corporation by donating a Titan X Pascal GPU used for this research.



1.1 A Parameters

For the covariance matrices A, Q the standard deviation STD is given to foster readability. As the noise is modeled to affect all axes in the same way, those values can be converted into the respective matrices by, e.g., A = (STD)2I.

Table 2 Parameters used

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nakath, D., Clemens, J. & Rachuy, C. Active Asteroid-SLAM. J Intell Robot Syst 99, 303–333 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: