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Absolute Navigation and Positioning of Mars Rover Using Gravity-Aided Odometry

Published online by Cambridge University Press:  23 November 2017

Jiandong Liu ,
Erhu Wei ,
Shuanggen Jin  and
Jingnan Liu
Jiandong Liu
Affiliation:
(Key Laboratory of Planetary Sciences, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China) (University of Chinese Academy of Sciences, Beijing 100049, China)
Erhu Wei*
Affiliation:
(School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China) (Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China)
Shuanggen Jin
Affiliation:
(Key Laboratory of Planetary Sciences, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China) (Department of Geomatics Engineering, Bulent Ecevit University, Zonguldak 67100, Turkey)
Jingnan Liu
Affiliation:
(GNSS Research Center, Wuhan University, Wuhan 430079, China)
*
(E-mail: [email protected])
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Abstract

Positioning and Navigation (PN) of Martian rovers still faces challenges due to limited observations. In this paper, the PN feasibilities of Mars rovers based on a Gravity-aided Odometry (GO) system are proposed and investigated in terms of numeric simulations and a case study. Statistical features of the Mars gravity field are studied to evaluate the feature diversity of the background map. The Iterative Closest Point (ICP) algorithm is introduced to match gravity measurements with the gravitational map. The trajectories of Mars Exploration Rovers (MER) and Mars Gravity Map 2011 (MGM2011) are used to complete the experiments. Several key factors of GO including odometry errors, measurement uncertainties, and grid resolution of the map are investigated to evaluate their influences on the positioning ability of the system. Simulated experiments indicate that the GO method could provide an alternative positioning solution for Martian surface rovers.

Keywords

Martian rover positioningPlanetary rover navigationIterative closest pointGravity-aided odometry
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 530 - 546
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Anderson, J.D., Efron, L. and Wong, S.K. (1970). Martian mass and Earth-Moon mass ratio from coherent S-band tracking of Mariners 6 and 7. Science, 167(3916), 277–279.Google Scholar
Arvidson, R.E., Anderson, R.C., Bartlett, P., Bell, J.F., Blaney, D., Christensen, P.R. and Ferguson, R. (2004). Localization and physical properties experiments conducted by Spirit at Gusev Crater. Science, 305(5685), 821–824.Google Scholar
Arvidson, R.E., Squyres, S.W., Anderson, R.C., Bell III, J.F., Blaney, D., Brueckner, J. and Clark, B.C. (2005). Overview of the Spirit Mars Exploration Rover Mission to Gusev Crater: Landing Site to Backstay Rock in the Columbia Hills. Journal of Geophysical Research: Planets (1991–2012), 111(E2), 1–22.Google Scholar
Ash, M.E., Shapiro, I.I. and Smith, W.B. (1967). Astronomical constants and planetary ephemerides deduced from radar and optical observations. The Astronomical Journal, 72(3), 338–350.Google Scholar
Besl, P.J. and McKay, N.D. (1992). A method for registration of 3-D shapes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 14(2), 239–256.Google Scholar
Champleboux, G., Lavallee, S., Szeliski, R. and Brunie, L. (1992). From accurate range imaging sensor calibration to accurate model-based 3D object localization. Computer Vision and Pattern Recognition, IEEE, 83–89.Google Scholar
Chen, Y. and Medioni, G. (1991). Object modelling by registration of multiple range images. Robotics and Automation, IEEE, 2724–2729.Google Scholar
Cheng, Y., Maimone, M.W. and Matthies, L. (2006). Visual odometry on the Mars exploration rovers-a tool to ensure accurate driving and science imaging. Robotics & Automation Magazine, 13(2), 54–62.Google Scholar
Creutzfeldt, B., Troch, P.A., Guntner, A., Ferre, T., Graeff, T. and Merz, B. (2014). Storage discharge relationships at different catchment scales based on local high precision gravimetry. Hydrological Processes, 28(3), 1465–1475.Google Scholar
Han, Y., Wang, B., Deng, Z., Wang, S., and Fu, M. (2017). A mismatch diagnostic method for TERCOM-based underwater gravity aided navigation. IEEE Sensors Journal, 17(9), 2880–2888.Google Scholar
Hirt, C., Claessens, S.J., Kuhn, M. and Featherstone, W.E. (2012). Kilometer-resolution gravity field of Mars: MGM2011. Planetary and Space Science, 67(1), 147–154.Google Scholar
Jin, S.G. and Zhang, T.Y. (2014). Automatic detection of impact craters on Mars using a modified adaboosting method. Planetary and Space Science, 99, 112–117.Google Scholar
Jin, S., Arivazhagan, S., and Araki, H. (2013). New results and questions of lunar exploration from SELENE, Chang'E-1, Chandrayaan-1 and LRO/LCROSS. Advances in Space Research, 52(2), 285–305.Google Scholar
Jircitano, A. and Dosch, D.E. (1991) Gravity aided inertial navigation system (GAINS). Institute of Navigation, 47th Annual Meeting, 1, 221–229.Google Scholar
Konopliv, A.S., Asmar, S.W., Folkner, W.M., Karatekin, Ö., Nunes, D.C., Smrekar, S.E., and Zuber, M.T. (2011). Mars high resolution gravity fields from MRO, Mars seasonal gravity, and other dynamical parameters. Icarus, 211(1), 401–428.Google Scholar
Lederer, M. (2009). Accuracy of the relative gravity measurement. Acta Geodyn Geomater, 6(3), 383–390.Google Scholar
Leonard, J.J. and Bahr, A. (2008). Autonomous underwater vehicle navigation. IEEE Journal of Oceanic Engineering, 35(3), 663–678.Google Scholar
Li, R., Di, K., Matthies, L.H., Folkner, W.M., Arvidson, R.E. and Archinal, B.A. (2004). Rover localization and landing-site mapping technology for the 2003 Mars exploration rover mission. Photogrammetric Engineering & Remote Sensing, 70(1), 77–90.Google Scholar
Li, R., Squyres, S.W., Arvidson, R.E., Archinal, B.A., Bell, J., Cheng, Y. and Golombek, M. (2005). Initial results of rover localization and topographic mapping for the 2003 Mars Exploration Rover mission. Photogrammetric Engineering & Remote Sensing, 71(10), 1129–1142.Google Scholar
Liu, J., Wei, E., and Jin, S. (2017). Mars Cruise Orbit Determination from Combined Optical Celestial Techniques and X-ray Pulsars. The Journal of Navigation, 70(4), 719–734.CrossRefGoogle Scholar
Maimone, M., Cheng, Y., Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics, 24(3), 169–186.Google Scholar
Matthies, L., Maimone, M., Johnson, A., Cheng, Y., Willson, R., Villalpando, C. and Angelova, A. (2007). Computer vision on Mars. International Journal of Computer Vision, 75(1), 67–92.Google Scholar
Matthies, L.H. (1989). Dynamic stereo vision. Doctoral Dissertation, Carnegie Mellon University Pittsburgh, PA, USA.Google Scholar
Pomerleau, F., Colas, F. and Siegwart, R. (2015). A review of point cloud registration algorithms for mobile robotics. Foundations and Trends in Robotics (FnTROB), 4(1), 1–104.CrossRefGoogle Scholar
Squyres, S.W., Arvidson, R.E., Bell, J.F., Bruckner, J., Cabrol, N.A., Calvin, W. and Des Marais, D.J. (2004). The Opportunity Rover's Athena science investigation at Meridiani Planum. Science, 306(5702), 1698–1703.Google Scholar
Squyres, S.W., Arvidson, R.E., Bollen, D., Bell, J.F., Brueckner, J., Cabrol, N.A. and Crumpler, L. (2006). Overview of the Opportunity Mars exploration rover mission to Meridiani Planum: Eagle Crater to Purgatory Ripple. Journal of Geophysical Research: Planets, 111(E12).Google Scholar
Tenzer, R., Eshagh, M. and Jin, S.G. (2015b). Martian sub-crustal stress from gravity and topographic models. Earth and Planetary Science Letters, 425, 84–92.Google Scholar
Tenzer, R., Chen, W., Tsoulis, D., Bagherbandi, M., Sjoberg, L., Novak, P. and Jin, S.G. (2015a), Analysis of the refined CRUST1·0 crustal model and its gravity field. Surveys in Geophysics, 36(1), 139–165.Google Scholar
Wang, B., Yu, L., Deng, Z., and Fu, M. (2016b). A particle filter-based matching algorithm with gravity sample vector for underwater gravity aided navigation. IEEE/ASME Transactions on Mechatronics, 21(3), 1399–1408.Google Scholar
Wang, B., Zhu, Y., Deng, Z., and Fu, M. (2016a). The gravity matching area selection criteria for under-water gravity-aided navigation application based on the comprehensive characteristic parameter. IEEE/ASME Transactions on Mechatronics, 21(6), 2935–2943.Google Scholar
Wang, H., Wang, Y., Fang, J., Chai, H. and Zheng, H. (2012). Simulation research on a minimum root-mean-square error rotation-fitting algorithm for gravity matching navigation. Science China Earth Sciences, 55(1), 90–97.CrossRefGoogle Scholar
Wei, E., Dong, C., Liu, J., Yang, Y., Tang, S., Gong, G. and Deng, Z. (2017). A robust solution of integrated SITAN with TERCOM algorithm: weight-reducing iteration technique for underwater vehicles' gravity-aided inertial navigation system. Navigation, 64, 111–122.Google Scholar
Wei, E., Jin, S.G., Zhang, Q., Liu, J., Li, X. and Yan, W. (2013). Autonomous navigation of Mars probe using X-ray pulsars: Modeling and results. Advances in Space Research, 51(5), 849–857.CrossRefGoogle Scholar
Zhang, Z. (1994). Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision, 13(2), 119–152.Google Scholar
Zheng, H., Wang, H., Wu, L., Chai, H. and Wang, Y. (2013). Simulation research on gravity-geomagnetism combined aided underwater navigation. The Journal of Navigation, 66(01), 83–98.Google Scholar