Extending Ground-Constraint LiDAR-IMU Calibration to Tilted Surfaces in a Continuous-Time Framework

• URL: http://arxiv.org/abs/2608.25135v1
• Date: Tue, 25 Aug 2026 20:35:18 GMT
• Title: Extending Ground-Constraint LiDAR-IMU Calibration to Tilted Surfaces in a Continuous-Time Framework
• Abstract summary: This paper presents a novel method that extends targetless LiDAR-IMU calibration for ground vehicles to non- flat environments.<n> typically necessitates full exci- tation of the sensor rig, a requirement that is not fulfilled by ground vehicles in normal operation.<n>To address the degenerate planar motion, state-of-the-art methods propose residuals that assume the colinearity of the gravity and physical surface normal vectors.
• Score: 5.115643891749577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel method that extends targetless LiDAR-IMU calibration for ground vehicles to non- flat environments. Calibration typically necessitates full exci- tation of the sensor rig, a requirement that is not fulfilled by ground vehicles in normal operation. To address the degenerate planar motion, state-of-the-art methods propose residuals that assume the colinearity of the gravity and physical surface normal vectors, restricting usage to cases where the ground is assumed flat. This paper proposes ground-plane residuals that do not require this assumption, and are applicable for planar motion on a tilted surface. Results are demonstrated on a dataset collected from a Husky ground vehicle, on the M2DGR dataset, as well as on an offroad vehicle dataset. Repeatability is shown to be improved both in tilted and flat-ground scenarios, with strong improvement demonstrated for the tilted case. The implementation and experiments are open-sourced at https://github.com/vkorotkine/licalib_tilted_ground.

Related papers

• Automated Hybrid Grounding Using Structural and Data-Driven Heuristics [68.08622864330296]
  Hybrid Grounding combines the strength of standard bottom-up grounding with recently proposed techniques where rule bodies are decoupled during grounding.<n>It has remained unclear when hybrid grounding shall use body-decoupled grounding and when to use standard bottom-up grounding.<n>We introduce a splitting algorithm based on data-structurals that detects when to use body-decoupled grounding and when standard grounding is beneficial.
  arXiv  Detail & Related papers  (2025-07-23T13:19:02Z)
• LiSu: A Dataset and Method for LiDAR Surface Normal Estimation [12.282805218676208]
  We present LiSu, the first large-scale, synthetic LiDAR point cloud dataset with ground truth surface normal annotations.<n>We also propose a novel annotated method that exploits the temporal characteristics of autonomous driving data to enhance surface normal estimation accuracy.<n>We demonstrate the effectiveness of our method on LiSu, achieving state-of-the-art performance in LiDAR surface normal estimation.
  arXiv  Detail & Related papers  (2025-03-11T16:35:22Z)
• Unified Human Localization and Trajectory Prediction with Monocular Vision [64.19384064365431]
  MonoTransmotion is a Transformer-based framework that uses only a monocular camera to jointly solve localization and prediction tasks.<n>We show that by jointly training both tasks with our unified framework, our method is more robust in real-world scenarios made of noisy inputs.
  arXiv  Detail & Related papers  (2025-03-05T14:18:39Z)
• 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)
• PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing [27.060381833488172]
  This paper presents a LiDAR bundle adjustment with progressive spatial smoothing. The effectiveness and robustness of our proposed approach have been validated on both simulation and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-10T07:56:54Z)
• Geo-Localization Based on Dynamically Weighted Factor-Graph [74.75763142610717]
  Feature-based geo-localization relies on associating features extracted from aerial imagery with those detected by the vehicle's sensors. This requires that the type of landmarks must be observable from both sources. We present a dynamically weighted factor graph model for the vehicle's trajectory estimation.
  arXiv  Detail & Related papers  (2023-11-13T12:44:14Z)
• Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection [92.75961303269548]
  The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD) We propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. Our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-11-03T02:21:35Z)
• Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines [3.337790639927531]
  We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle. We model the estimation process as a robust LS optimization problem which can be reformulated as a linear problem. We validate the approach on our research vehicle in real-world scenarios.
  arXiv  Detail & Related papers  (2022-03-02T15:26:51Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Surface Normal Estimation of Tilted Images via Spatial Rectifier [23.112310775824714]
  Existing approaches exhibit bounded performance on predicting surface normals because they were trained using gravity-aligned images. Our two main hypotheses are: (1) visual scene layout is indicative of the gravity direction; and (2) not all surfaces are equally represented by a learned estimator due to the structured distribution of the training data. We design a spatial estimator that is learned to transform the surface normal distribution of a tilted image to the one that matches the gravity-aligned data distribution.
  arXiv  Detail & Related papers  (2020-07-17T22:22:34Z)

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.