RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback

• URL: http://arxiv.org/abs/2609.03720v1
• Date: Thu, 03 Sep 2026 11:54:16 GMT
• Title: RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
• Abstract summary: This paper presents a method for real-time vibration-aware traversability mapping using a LiDAR point cloud and Inertial Measurement Unit (IMU) measurements.<n>The approach is evaluated in a lunar analogue environment, an outdoor field, and an underground mine.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data [49.62628745057007]
  We propose a simulation-based inference approach to terrain parameter inversion from radar sounder data.<n>Synthetic observations from a GPU-based simulator are used to train a neural network-based density estimator for neural posterior estimation.<n>We demonstrate that our NPE model is well calibrated on simulated data and transferable to real Mars radar profiles.
  arXiv  Detail & Related papers  (2026-05-05T08:41:02Z)
• Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection [48.120690574682875]
  We present a metric RGB-based geometric-semantic reconstruction and traversability analysis framework for aerial-to-ground hidden space inspection.<n>Experiments on a tethered UAV-UGV platform demonstrate reliable deployment-zone identification in hidden space scenarios.
  arXiv  Detail & Related papers  (2026-03-15T22:20:59Z)
• Loc$^2$: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching [80.57282092735991]
  We propose an accurate and interpretable fine-grained cross-view localization method.<n>It estimates the 3 Degrees of Freedom (DoF) pose of a ground-level image by matching its local features with a reference aerial image.<n> Experiments show state-of-the-art accuracy in challenging scenarios such as cross-area testing and unknown orientation.
  arXiv  Detail & Related papers  (2025-09-11T18:52:16Z)
• LunarLoc: Segment-Based Global Localization on the Moon [52.418394025548295]
  Global localization is necessary for autonomous operations on the lunar surface where traditional Earth-based navigation infrastructure, such as GPS, is unavailable.<n>We propose LunarLoc, an approach to global localization that leverages instance segmentation for zero-shot extraction of boulder landmarks from onboard stereo imagery.<n>LunarLoc achieves sub-cm level accuracy in multi-session global localization experiments, significantly outperforming the state of the art in lunar global localization.
  arXiv  Detail & Related papers  (2025-06-20T12:06:47Z)
• Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization [1.9684593154403558]
  We propose a novel LIDAR-based localization framework, which achieves high accuracy and provides robust localization in 3D pointcloud maps.<n>The system integrates global information with LIDAR-based odometry to optimize the localization state.<n>The algorithm is tested on various maps of different data sets and has higher robustness and accuracy than other localization algorithms.
  arXiv  Detail & Related papers  (2025-03-29T19:41:31Z)
• Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process [15.756895046886406]
  We propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to navigate through complex terrains.<n>Our method outper- forms SOTA approaches in both accuracy and computational efficiency.<n>We develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor envi- ronments.
  arXiv  Detail & Related papers  (2025-03-06T06:26:57Z)
• ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle [49.61982102900982]
  A LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains.<n>A global-scale factor graph is established to aid in the reduction of cumulative errors.<n>The results demonstrate that ROLO-SLAM excels in pose estimation of ground vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks.
  arXiv  Detail & Related papers  (2025-01-04T02:44:27Z)
• Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing [0.0]
  This paper develops a novel real-time planetary terrain mapping algorithm.<n>It accounts for topographic uncertainty between the sampled points, or the uncertainty due to sparse 3D measurements.
  arXiv  Detail & Related papers  (2024-09-14T05:12:14Z)
• LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning [59.17191114000146]
  LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs) In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit ( CPU) which may be located in the cloud. Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps.
  arXiv  Detail & Related papers  (2022-02-01T20:27:46Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation [0.0]
  We present an algorithm that is completely detached from external assistance and runs fully automatically. Our method focuses on the high-precision fusion of LiDAR point clouds. Experiments in simulation as well as with real measurements have shown that our algorithm performs a continuous point cloud registration of up to four 64-layer LiDARs in real-time.
  arXiv  Detail & Related papers  (2020-07-28T08:43:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.