Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.6.1.3

Descent Dataset Generation and Landmark Extraction for Terrain Relative Navigation on Mars  

Kim, Jae-In (KPS Satellite Development Division, Korea Aerospace Research Institute)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 1015-1023 More about this Journal
Abstract
The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.
Keywords
Terrain relative navigation; Mars landing; Descent image; Landmark extraction;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool, 2008. Speeded-up robust features (SURF), Computer Vision and Image Understanding, 110(3): 346-359. https://doi.org/10.1016/j.cviu.2007.09.014   DOI
2 Johnson, A., S. Aaron, J. Chang, Y. Cheng, J. Montgomery, S. Mohan, S. Schroeder, B. Tweddle, N. Trawny, and J. Zheng, 2017. The Lander vision system for Mars 2020 entry descent and landing, Proc. of 2017 AAS Guidance Navigation and Control Conference, Breckenridge, CO, Feb. 2-7.
3 Kang, S. and Y. Cheon, 2021. Current status of international Mars exploration activities, Current Industrial and Technological Trends in Aerospace, 19(2): 131-141 (in Korean with English abstract).
4 Kim, J. and H. Kim, 2018. Performance Comparison of Matching Cost Functions for High-Quality SeaIce Surface Model Generation, Korean Journal of Remote Sensing, 34(6-2): 1251-1260 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2018.34.6.2.9   DOI
5 Lee, H., D. Rew, and G. Ju, 2018. Trend of image processing technology for entry and descent of planetary spacecraft, Current Industrial and Technological Trends in Aerospace, 16(1): 90-100 (in Korean with English abstract).
6 Rublee, E., V. Rabaud, K. Konolige, and G. Bradski, 2011. ORB: An efficient alternative to SIFT or SURF, Proc. of 2011 IEEE International Conference on Computer Vision, Barcelona, Spain, Nov. 6-13, pp. 2564-2571. https://doi.org/10.1109/ICCV.2011.6126544   DOI
7 Wu, B., J. Dong, Y. Wang, W. Rao, Z. Sun, Z. Li, Z. Tan, Z. Chen, C. Wang, W.C. Liu, L. Chen, J. Zhu, and H. Li, 2022. Landing site selection and characterization of Tianwen-1 (Zhurong rover) on Mars, Journal of Geophysical Research: Planets, 127(4): 1-17. https://doi.org/10.1029/2021JE007137   DOI
8 Malakhov, I.M.A., M. Djachkova, D. Golovin, M. Litvak, M. Mokrousov, A. Sanin, H. Svedhem, and L. Zelenyi, 2022. The evidence for unusually high hydrogen abundances in the central part of Valles Marineris on Mars, Icarus, 374(2022): 114805. https://doi.org/10.1016/j.icarus.2021.114805   DOI
9 Fischler, M.A. and R.C. Bolles, 1981. Random Sample Consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24(6): 381-395. https://doi.org/10.1145/358669.358692   DOI
10 Kim, J., T. Kim, D. Shin, and S. Kim, 2017. Fast and robust geometric correction for mosaicking UAV images with narrow overlaps, International Journal of Remote Sensing, 38(8-10): 2557-2576. https://doi.org/10.1080/01431161.2017.1294779   DOI