Research and ground-based validation on Mars rover localization based on multi-level images matching

doi:10.11947/j.AGCS.2023.20210635

Abstract

Abstract: The high-precision positioning of the rover in deep space exploration is essential for its safety and the success of the exploration missions. Two types of positioning methods for rovers are commonly used: relative and absolute positioning. The absolute positioning method mainly uses the orbiter/lander images as a reference to determine the rover location in the global coordinate system. Considering that an unmanned aerial vehicle (UAV) was carried in the Mars 2020 Project for the first time and finally verified in the Mars environment, high-resolution images from the UAV can therefore be used for rover localization. In this paper, a novel localization framework for Mars rover was proposed by matching multi-source images from orbiter, UAV and the rover respectively. Firstly, the structure from motion method was introduced to reconstruct the regional 3D high-resolution terrain from UAV images. Furthermore, a multi-view matching strategy was designed for UAV and rover images to determine the rover position in the reconstructed local 3D map by the space resection method. Finally, the rover position in the global coordinate system can be obtained by matching the UAV with the orbiter images. To verify the effectiveness of the proposed method, a simulated experiment was designed based on the test field for deep space exploration in Tongji university. The experimental result shows that the proposed localization method can generate high accuracy positions for Mars exploration with the assistance of high-resolution UAV images, and therefore support the following similar exploration missions for rovers.

Key words: deep space exploration, Mars UAV, aerial-ground image matching, space resection with large angle, rover global localization

CLC Number:

P237

CAO Zilong, TONG Xiaohua, XU Xiong, YE Zhen, XIAO Changjiang. Research and ground-based validation on Mars rover localization based on multi-level images matching[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(4): 579-587.

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