用视觉光流和激光测距实现小月球探测器轻量化自主导航
Vision-Guided Optic Flow Navigation for Small Lunar Missions
- 融合光流与激光测距，基于平面/球面地形建模估计运动
- 复杂地形速度误差低于10%，典型地形误差仅1%左右
- 可在小月球着陆器的低算力条件下实时运行
小型月球任务面临在质量、功耗和计算资源严格受限下的鲁棒自主导航挑战。本文提出一种基于运动场反演的轻量级方案，利用光流与基于激光测距的深度估计，在仅依赖CPU的情况下实现月球下降过程中的自身运动估计。通过将经典光流方法扩展至适用于月球/行星接近、下降和着陆阶段的几何特征，采用激光测距仪参数化的平面与球面地形近似模型。运动场反演采用最小二乘框架，结合金字塔Lucas-Kanade算法提取的稀疏光流特征。我们在合成生成的月球南极复杂地形图像上验证了该方法，使用符合小型月球着陆器算力限制的CPU预算。结果表明，从接近到着陆全程均可实现高精度速度估计：复杂地形下误差低于10%，典型地形下误差约1%，且满足实时应用要求。该框架为小型月球任务提供了具备鲁棒性的轻量化星载导航潜力。
原文摘要 · Abstract (English)
Private lunar missions are faced with the challenge of robust autonomous navigation while operating under stringent constraints on mass, power, and computational resources. This work proposes a motion-field inversion framework that uses optical flow and rangefinder-based depth estimation as a lightweight CPU-based solution for egomotion estimation during lunar descent. We extend classical optical flow formulations by integrating them with depth modeling strategies tailored to the geometry for lunar/planetary approach, descent, and landing, specifically, planar and spherical terrain approximations parameterized by a laser rangefinder. Motion field inversion is performed through a least-squares framework, using sparse optical flow features extracted via the pyramidal Lucas-Kanade algorithm. We verify our approach using synthetically generated lunar images over the challenging terrain of the lunar south pole, using CPU budgets compatible with small lunar landers. The results demonstrate accurate velocity estimation from approach to landing, with sub-10% error for complex terrain and on the order of 1% for more typical terrain, as well as performances suitable for real-time applications. This framework shows promise for enabling robust, lightweight on-board navigation for small lunar missions.
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