基于数字高程模型锚定的月球表面视觉SLAM
Visual SLAM with DEM Anchoring for Lunar Surface Navigation
AI总结：
本文提出一种结合学习特征检测与DEM全局约束的视觉SLAM系统，用于月球表面长距离导航，通过引入高程和表面法线因素缓解长期漂移问题。
摘要：
未来月球任务需要具备自主导航能力的探测车，能够在复杂地形上行驶数十公里并保持精确定位和一致地图。然而，缺乏全球定位系统、极端光照和低纹理月壤使月球远距离导航尤其困难，视觉惯性里程计在长距离行驶中积累漂移。为此，我们提出一种立体视觉同时定位与建图（SLAM）系统，整合了学习特征检测与匹配和来自数字高程模型（DEMs）的全局约束。前端采用学习方法提取和匹配特征以提高对极端光照和重复地形的鲁棒性，后端将DEMs推导出的高程和表面法线因素纳入位姿图，提供绝对表面约束以缓解长期漂移。我们通过Unreal Engine生成的模拟月球行驶数据和从埃特纳山收集的真实月球/火星类比数据验证了我们的方法。结果表明，DEMs锚定方法在基准SLAM方法中一致降低了绝对轨迹误差，即使在重复或视觉模糊地形中也减少了漂移。
英文摘要：
Future lunar missions will require autonomous rovers capable of traversing tens of kilometers across challenging terrain while maintaining accurate localization and producing globally consistent maps. However, the absence of global positioning systems, extreme illumination, and low-texture regolith make long-range navigation on the Moon particularly difficult, as visual-inertial odometry pipelines accumulate drift over extended traverses. To address this challenge, we present a stereo visual simultaneous localization and mapping (SLAM) system that integrates learned feature detection and matching with global constraints from digital elevation models (DEMs). Our front-end employs learning-based feature extraction and matching to achieve robustness to illumination extremes and repetitive terrain, while the back-end incorporates DEM-derived height and surface-normal factors into a pose graph, providing absolute surface constraints that mitigate long-term drift. We validate our approach using both simulated lunar traverse data generated in Unreal Engine and real Moon/Mars analog data collected from Mt. Etna. Results demonstrate that DEM anchoring consistently reduces absolute trajectory error compared to baseline SLAM methods, lowering drift in long-range navigation even in repetitive or visually aliased terrain.
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