RoughSense: 点群とIMUフィードバックを用いた軽量な地形起因ローバ振動予測
RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
LiDAR点群とIMU測定を用いて、ローバの振動をリアルタイムに予測する軽量な traversability マッピング手法を提案。RANSACによる幾何学的予測を再帰的最小二乗法で補正し、月面類似環境や地下鉱山で評価した。
著者: Gabriel Manuel Garcia, Stephanie Aravecchia, Miguel Angel Olivares-Mendez
分類: cs.RO
原文アブストラクト
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.