RoughSense：基于点云和IMU反馈的轻量级地形诱导漫游车振动预测方法
RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
- University of Luxembourg（卢森堡大学）
- IRL Georgia Tech-CNRS（乔治亚理工学院-法国国家科学研究中心国际研究实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
RoughSense利用LiDAR点云和IMU反馈，结合RANSAC、SLAM与递归最小二乘，在三类场景实现轻量级漫游车地形诱导振动预测与可通行性建图，保障受限环境自主导航安全。
AI中文摘要：
太空自主导航需要可靠的地形评估以保障安全作业，尤其适用于通信、计算资源和功耗预算有限的地下环境。本文提出一种轻量级方法，利用激光探测与测距（LiDAR）点云和惯性测量单元（IMU）测量数据实现实时感知振动的可通行性建图。首先，对同步定位与建图（SLAM）算法生成的局部点云块应用随机抽样一致性（RANSAC），从地形几何中估算初始振动代理；同时，IMU提供漫游车行驶时经历振动的局部观测值。随后，采用递归最小二乘（Recursive Least Squares）在线校正基于点云的预测，使系统能调整几何估计以匹配实测的漫游车响应。该方法在月球模拟环境、户外场地和地下矿井中进行了评估。
英文摘要：
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.
