Lidar-Aided Camera Feature Tracking and Visual SLAM for Spacecraft Low-Orbit Navigation and Planetary Landing

  • Franz AndertEmail author
  • Nikolaus Ammann
  • Bolko Maass


This paper explores the state estimation problem for an autonomous precision landing approach on celestial bodies. This is generally based on sensor fusion from inertial and optical sensor data. Independent of the state estimation filter, a remaining problem is the provision of position updates without the use of known absolute support information as it appears when the vehicle navigates within unknown terrain. Visual odometry or simultaneous localization and mapping (SLAM) approaches typically provide relative position. This is quite suitable, but it can be adverse due to error accumulation. The presented method combines monocular camera images with laser distance measurements to allow visual SLAM without errors from increasing scale uncertainty. It is shown that this reduces the accumulated error in comparison to sole monocular visual SLAM. Further, the presented method integrates the matching to known landmarks if they are available in the beginning of a landing approach so that the relative optical navigation can be initialized without systematic errors. Finally, tests with a simulated moon landing are performed and it is shown that the method is capable of navigating down to the ground impact.


Unscented Kalman Filter Visual Odometry Reprojection Error Unmanned Helicopter Relative Navigation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Achtelik, M., Achtelik, M., Weiss, S., Siegwart, R.: Onboard imu and monocular vision based control for MAVs in unknown in- and outdoor environments. In: IEEE International Conference on Robotics and Automation, pp. 3056–3063 (2012)Google Scholar
  2. 2.
    Andert, F., Ammann, N., Püschel, J., Dittrich, J.: On the safe navigation problem for unmanned aircraft: Visual odometry and alignment optimizations for UAV positioning. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 734–743 (2014)Google Scholar
  3. 3.
    Araki, H., Tazawa, S., Noda, H., Ishihara, Y., Goossens, S., Sasaki, S., Kawano, N., Kamiya, I., Otake, H., Oberst, J., Shum, C.: Lunar global shape and polar topography derived from Kaguya-LALT laser altimetry. Science 323(5916), 897–900 (2009)CrossRefGoogle Scholar
  4. 4.
    Davies, M.E., Colvin, T.R.: Lunar coordinates in the regions of the apollo landers. Journal of Geophysical Research 105(E8), 20:277–20:280 (2000)Google Scholar
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Graphics and Image Processing 24(6), 381–395 (1981)MathSciNetGoogle Scholar
  6. 6.
    Fraundorfer, F., Scaramuzza, D.: Visual odometry – part II: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine 19(2), 78–90 (2012)CrossRefGoogle Scholar
  7. 7.
    Hartley, R., Sturm, P.: Triangulation. Computer Vision and Image Understanding 68, 146–157 (1997)CrossRefGoogle Scholar
  8. 8.
    Krause, S., Evert, R.: Remission based improvement of extrinsic parameter calibration of camera and laser scanner. In: 12th International Conference on Control, Automation, Robotics & Vision, pp. 829–834 (2012)Google Scholar
  9. 9.
    Krüger, H., Theil, S.: TRON – hardware-in-the-loop test facility for lunar descent and landing optical navigation. In: 18th IFAC Symposium on Automatic Control in Aerospace (2010)Google Scholar
  10. 10.
    Kwon, Y.H.: Direct linear transform method (1998), url:
  11. 11.
    Lu, F., Hartley, R.: A fast optimal algorithm for l 2 triangulation. In: Asian Conf. on Computer Vision (2007)Google Scholar
  12. 12.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  13. 13.
    Maass, B., Krüger, H., Theil, S.: An edge-free, scale-, pose- and illumination-invariant approach to crater detection for spacecraft navigation. In: 7th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 603–608 (2011)Google Scholar
  14. 14.
    Paproth, C., Schlüßler, E., Scherbaum, P., Börner, A.: Sensor++: Simulation of remote sensing systems from visible to thermal infrared. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXII ISPRS Congress, pp. 257–260 (2012)Google Scholar
  15. 15.
    Sabiron, G., Chavent, P., Burlion, L., Kervendal, E., Bornschlegl, E., Fabiani, P., Raharijaona, T., Ruffier, F.: Toward an autonomous lunar landing based on low-speed optic flow sensors. In: Chu, Q., et al. (eds.) Advances in Aerospace Guidance, Navigation and Control: Selected Papers from the CEAS EuroGNC Conf., pp. 681–699. Springer (2013)Google Scholar
  16. 16.
    Samaan, M., Theil, S.: Development of a low cost star tracker for the SHEFEX mission. Aerospace Science and Technology 23(1), 469–478 (2012)CrossRefGoogle Scholar
  17. 17.
    Scaramuzza, D., Fraundorfer, F.: Visual odometry – part I: The first 30 years and fundamentals. IEEE Robotics & Automation Magazine 18(4), 80–92 (2011)CrossRefGoogle Scholar
  18. 18.
    Shen, S., Mulgaonkar, Y., Michael, N., Kumar, V.: Vision-based state estimation and trajectory control towards high-speed flight with a quadrotor. In: Robotics: Science and Systems (2013)Google Scholar
  19. 19.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
  20. 20.
    Tran, T., Rosiek, M.R., Beyer, R.A., Mattson, S., Howington-Kraus, E., Robinson, M., Archinal, B.A., Edmundson, K., Harbour, D., Anderson, E., the LROC Science Team: Generating digital terrain models using LROC NAC images. In: Proc. of Special joint symposium of ISPRS Technical Commission IV & AutoCarto / ASPRS/CaGIS 2010 Fall Specialty Conference (2010)Google Scholar
  21. 21.
    Verveld, M.J.: Relative optical navigation for a lunar lander mission. In: Chu, Q., et al. (eds.) Advances in Aerospace Guidance, Navigation and Control: Selected Papers from the CEAS EuroGNC Conf., pp. 661–679. Springer (2013)Google Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.German Aerospace Center (DLR), Institute of Flight SystemsBraunschweigGermany
  2. 2.German Aerospace Center (DLR), Institute of Space SystemsBremenGermany

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