外观
论文arXiv 2609.037201 个月前
RoughSense：使用点云和 IMU 反馈的轻量级地形诱发巡视器振动预测
原文链接：RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback（arXiv 2609.03720 · 发布日期：2026-09-03）
RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
面向空间自主导航地形评估，RoughSense 结合 LiDAR 点云几何与 IMU 反馈，在线校正振动通行性地图，并在月面类比、户外和地下矿井评估。
核心要点
- 地下等空间环境通信、算力和能耗受限，需要可靠地形评估保障安全自主导航。
- 方法从 SLAM 生成的局部点云片用 RANSAC 估计几何振动代理，并用 IMU 观测实际振动。
- 系统通过递归最小二乘在线修正点云预测，使几何估计适应实测巡视器响应。
- 论文在月面类比环境、户外场地和地下矿井中评估该轻量实时通行性建图方法。
验证范围与局限：验证范围为月面类比环境、户外场地和地下矿井，摘要未说明具体任务或定量结果。
英文摘要 (Abstract)
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.
作者：Gabriel Manuel Garcia, Stéphanie Aravecchia, Miguel A. Olivares-Méndez
来源
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