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Modeling and Matching of Landmarks for Automation of Mars Rover Localization

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2008, Doctor of Philosophy, Ohio State University, Geodetic Science and Surveying.
The Mars Exploration Rover (MER) mission, begun in January 2004, has been extremely successful. However, decision-making for many operation tasks of the current MER mission and the 1997 Mars Pathfinder mission is performed on Earth through a predominantly manual, time-consuming process. Unmanned planetary rover navigation is ideally expected to reduce rover idle time, diminish the need for entering safe-mode, and dynamically handle opportunistic science events without required communication to Earth. Successful automation of rover navigation and localization during the extraterrestrial exploration requires that accurate position and attitude information can be received by a rover and that the rover has the support of simultaneous localization and mapping. An integrated approach with Bundle Adjustment (BA) and Visual Odometry can efficiently refine the rover position. However, during the MER mission, BA is done manually because of the difficulty in the automation of the cross-site tie points selection. This dissertation proposes an automatic approach to select cross-site tie points from multiple rover sites based on the methods of landmark extraction, landmark modeling, and landmark matching. The first step in this approach is that important landmarks such as craters and rocks are defined. Methods of automatic feature extraction and landmark modeling are then introduced. Complex models with orientation angles and simple models without those angles are compared. The results have shown that simple models can provide reasonably good results. Next, the sensitivity of different modeling parameters is analyzed. Based on this analysis, cross-site rocks are matched through two complementary stages: rock distribution pattern matching and rock model matching. In addition, a preliminary experiment on orbital and ground landmark matching is also briefly introduced. Finally, the reliability of the cross-site tie points selection is validated by fault detection, which considers the mapping capability of MER cameras and the reason for mismatches. Fault detection strategies are applied in each step of the cross-site tie points selection to automatically verify the accuracy. The mismatches are excluded and localization errors are minimized. The method proposed in this dissertation is demonstrated with the datasets from the 2004 MER mission (traverse of 318 m) as well as the simulated test data at Silver Lake (traverse of 5.5 km), California. The accuracy analysis demonstrates that the algorithm is efficient at automatically selecting a sufficient number of well-distributed high-quality tie points to link the ground images into an image network for BA. The method worked successfully along with a continuous 1.1 km stretch. With the BA performed, highly accurate maps can be created to help the rover to navigate precisely and automatically. The method also enables autonomous long-range Mars rover localization.
Ron (Rongxing Li) Li, PhD (Committee Chair)
Tony Schenk, PhD (Committee Member)
Alper Yilmaz, PhD (Committee Member)
191 p.

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Citations

  • Wang, J. (2008). Modeling and Matching of Landmarks for Automation of Mars Rover Localization [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1213192082

    APA Style (7th edition)

  • Wang, Jue. Modeling and Matching of Landmarks for Automation of Mars Rover Localization. 2008. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1213192082.

    MLA Style (8th edition)

  • Wang, Jue. "Modeling and Matching of Landmarks for Automation of Mars Rover Localization." Doctoral dissertation, Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1213192082

    Chicago Manual of Style (17th edition)