Problem
Research questions and friction points this paper is trying to address.
autonomous navigation
terrain assessment
vibration prediction
LiDAR point cloud
IMU
RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
🤖 AI Summary
为解决太空自主导航中地形评估问题，提出了一种基于LiDAR点云和IMU数据的轻量级实时振动预测方法。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Lightweight Vibration Prediction
Point Clouds
IMU Feedback
RANSAC
Recursive Least Squares
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