Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner

Hugging Face Daily Papers Papers

Summary

A paper proposing FLISP, a mapless path planning framework for cooperative UGV-UAV tunnel inspection using a single LiDAR-IMU suite, achieving 100% success rate and 7ms latency in a 1.2km tunnel.

Hydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast LiDAR-IMU Synchronized Path Planner), a mapless planning framework for cooperative UGV-UAV inspection. Unlike traditional map-based paradigms, FLISP features three core contributions: (1) a unified architecture where a single UGV-mounted LiDAR-IMU suite drives synchronized path generation for both platforms; (2) platform-specific solvers utilizing an enhanced Firefly Algorithm for UGV obstacle avoidance and a dynamic iterative optimizer for UAV flight; and (3) a hierarchical refinement strategy ensuring kinematic feasibility without state estimation drift. Benchmarks in a 1.2 km operational tunnel demonstrate that FLISP circumvents structural bottlenecks of map-based methods, eliminating map rasterization overhead (Fast-LIO2 + A*) and sampling instability (LIO-SAM + RRT*). FLISP achieves a 100% success rate with 7 ms latency, representing a 7-fold speedup over grid-based and a three-order-of-magnitude improvement over sampling-based baselines. Validated in operational hydropower tunnels, this approach offers a scalable solution for robotic inspection in feature-degraded linear infrastructure. A demonstration video is available at https://youtu.be/Y_ezs1PfLJ4, and the code at https://github.com/ArchibaldGuo/FLISP.git.
Original Article
View Cached Full Text

Cached at: 06/30/26, 11:35 AM

Paper page - Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner

Source: https://huggingface.co/papers/2606.25393

Abstract

Hydropowertunnelinspectioniscriticalforinfrastructureintegrityyetremainsinefficientandhazardoususingmanualmethods.WeproposeFLISP(FastLiDAR-IMUSynchronizedPathPlanner),amaplessplanningframeworkforcooperativeUGV-UAVinspection.Unliketraditionalmap-basedparadigms,FLISPfeaturesthreecorecontributions:(1)aunifiedarchitecturewhereasingleUGV-mountedLiDAR-IMUsuitedrivessynchronizedpathgenerationforbothplatforms;(2)platform-specificsolversutilizinganenhancedFireflyAlgorithmforUGVobstacleavoidanceandadynamiciterativeoptimizerforUAVflight;and(3)ahierarchicalrefinementstrategyensuringkinematicfeasibilitywithoutstateestimationdrift.Benchmarksina1.2kmoperationaltunneldemonstratethatFLISPcircumventsstructuralbottlenecksofmap-basedmethods,eliminatingmaprasterizationoverhead(Fast-LIO2+A*)andsamplinginstability(LIO-SAM+RRT*).FLISPachievesa100%successratewith7mslatency,representinga7-foldspeedupovergrid-basedandathree-order-of-magnitudeimprovementoversampling-basedbaselines.Validatedinoperationalhydropowertunnels,thisapproachoffersascalablesolutionforroboticinspectioninfeature-degradedlinearinfrastructure.Ademonstrationvideoisavailableathttps://youtu.be/Y_ezs1PfLJ4,andthecodeathttps://github.com/ArchibaldGuo/FLISP.git.

View arXiv pageView PDFAdd to collection

Get this paper in your agent:

hf papers read 2606\.25393

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2606.25393 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2606.25393 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2606.25393 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving

arXiv cs.CL

Fast-dDrive is a block-diffusion VLA model for end-to-end autonomous driving that achieves state-of-the-art trajectory accuracy while delivering over 12x throughput speedup over autoregressive baselines, addressing the trade-off between high-fidelity planning and efficient inference for edge deployment.

A better method for planning complex visual tasks

MIT News — Artificial Intelligence

MIT researchers developed VLMFP, a two-stage generative AI approach combining vision-language models with formal planning software to achieve 70% success rate on complex visual planning tasks like robot navigation, nearly 2.3x better than existing baselines. The method automatically translates visual scenarios into planning files that classical solvers can process, enabling effective long-horizon planning in novel environments.