During lunar surface scientific explorations, high-accuracy self-localization of lunar rovers is a key problem to be solved. Aiming at accurate localization in the featureless environments on the lunar surface, we propose a new visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm, which fuses the measurements of vision and the inertial sensor by pose-graph optimization to achieve high-precision self-localization. An optical flow tracking algorithm based on the quadtree method is proposed to address the unbounded front-end visual measurements correlation error in featureless environments. This algorithm can effectively track robust feature points, thereby improving the accuracy of pose estimation between adjacent frames. Moreover, an effective star point removal algorithm is proposed to effectively remove the star points at infinity, which is beneficial to solve localization accuracy decrease caused by unstable landmarks at infinity. A computer simulation system of the lunar surface environment as well as a set of various lunar visual inertial SLAM simulation datasets is further built, and several localization tests in different lunar simulation environments are conducted. Simulation results verify that our algorithm is more robust with better localization accuracy.
XIE Hongle
,
CHEN Weidong
,
FAN Yaxian
,
WANG Jingchuan
. Visual-inertial SLAM in featureless environments on lunar surface[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021
, 42(1)
: 524169
-524169
.
DOI: 10.7527/S1000-6893.2020.24169
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