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Feature-Based Simultaneous Localization and Mapping for LiDAR-Equipped Lunar Surface Mobility Systems Using Random Finite Set Filtering
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DOI：10.1109/taes.2026.3715601.png)
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Abstract
En 中文
There is a renewed international effort to establish a sustained human presence on the lunar surface, e.g., the confirmed existence of water ice at the lunar poles has rendered the exploration of these regions particularly compelling. Current proposed missions predominantly employ robotic rovers, the majority of which operate in low-latitude environments. In contrast, high-latitude exploration is impeded by challenging illumination conditions, which NASA is currently mitigating through the deployment of high-power illuminators and passive imaging systems. This work investigates alternative sensing modalities independent of the lunar surface’s extreme lighting conditions. Specifically, it evaluates the use of scanning-mirror light detection and ranging (LiDAR) for terrain-relative navigation, with the objective of reducing drift errors inherent to dead-reckoning techniques. In addition, it examines simultaneous localization and mapping (SLAM) strategies that incorporate multiobject tracking frameworks to address measurement uncertainty, missed detections, environmental clutter, and unknown data associations. To this end, several extensions of the probability hypothesis density (PHD) SLAM algorithm are developed to adapt it for the lunar rover application. These extensions include 3-D state modeling, the use of ground-affine landmarks, explicit clutter representation, and the decoupling of odometry and exteroceptive sensor frames. The extended algorithm is integrated into a comprehensive localization pipeline encompassing high-fidelity sensor modeling and pose estimation. Simulation studies demonstrate that PHD-SLAM consistently reduces localization error relative to odometry alone. Furthermore, PHD-SLAM achieves accurate pose estimation with noisy limited field-of-view LiDAR measurements in scenarios where point cloud alignment techniques, such as iterative closest point, fail to converge.
Keywords:
Autonomous
feature detection
filtering
light detection and ranging (LiDAR)
random finite sets (RFSs)
simultaneous localization and mapping (SLAM)
space vehicle navigation
Journal
IF:
5.7
Papers:
651
Citations:
2.4W
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