用点云和惯性数据实时预测月球车振动，提升复杂地形导航安全性。
RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
- 通过点云几何分析生成振动初步预测，结合SLAM局部扫描
- 用IMU实测振动数据在线修正预测，提升准确性
- 轻量化设计适合资源受限的太空探测场景
在通信、计算资源和功耗受限的地下空间环境中，自主导航需可靠地形评估以确保安全。本文提出一种轻量级实时振动感知可通行性映射方法，利用激光雷达（LiDAR）点云与惯性测量单元（IMU）数据。首先通过随机采样一致性（RANSAC）对SLAM算法生成的局部点云块进行处理，估算初始振动代理值；同时，IMU实时记录巡视器行进过程中的振动响应。随后采用递归最小二乘法在线校正点云预测结果，使几何估计能动态适配实际振动反馈。该方法在月球类比环境、户外场地及地下矿井中进行了验证。
原文摘要 · Abstract (English)
Autonomous navigation in space requires reliable terrain assessment for safe operations, especially in underground environments with limited communication, computing resources, and power budget. This paper presents a lightweight method for real-time vibration-aware traversability mapping using a Light Detecting And Ranging (LiDAR) point cloud and Inertial Measurement Unit (IMU) measurements. An initial vibration proxy is estimated from terrain geometry by applying Random sample consensus (RANSAC) to local point-cloud patches produced by a Simultaneous Localisation And Mapping (SLAM) algorithm. In parallel, the IMU provides local observations of the vibration experienced by the rover during traversal. The point-cloud-based prediction is then corrected online using Recursive Least Squares, allowing the system to adapt the geometric estimate to the measured rover response. The approach is evaluated in a lunar analogue environment, an outdoor field, and an underground mine.
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